UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting | Paper Citation Record

Observation 85f95615-5d4c-4de4-8d50-7b9a0f2042ea · outbound

This paper cites 3d semantic parsing of large-scale indoor spaces.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting 3d semantic parsing of large-scale indoor spaces

Reference 1

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Observation 346e050c-3f32-4c80-be32-1a4ac5c642b0 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting ShapeNet: An Information-Rich 3D Model Repository

Reference 2

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Observation 96bb3114-22d0-445d-8bac-e7f463e8ee92 · outbound

This paper cites pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction

Reference 3

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Observation d3ead491-f6ac-45f2-9f02-4fd0ca6fd9d8 · outbound

This paper cites Decoupled Local Aggregation for Point Cloud Learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Decoupled Local Aggregation for Point Cloud Learning

Reference 4

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Observation 3a0a8290-a7b9-4484-aaf9-204c45b41692 · outbound

This paper cites Pointgpt: Auto-regressively generative pre- training from point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointgpt: Auto-regressively generative pre- training from point clouds

Reference 5

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Observation 3682601a-e5b7-4276-8937-ddd6377f0240 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting A simple framework for contrastive learning of visual representations

Reference 6

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Observation f4485708-b636-4d21-844a-2bff2f24f0f6 · outbound

This paper cites Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images

Reference 7

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Observation 848b19e2-9ff2-4726-a17c-cc9dc24f8093 · outbound

This paper cites Unit3d: A unified transformer for 3d dense captioning and visual grounding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Unit3d: A unified transformer for 3d dense captioning and visual grounding

Reference 8

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Observation b84984e0-b844-4205-a0fd-d1cdf67bdd69 · outbound

This paper cites 4d spatio-temporal convnets: Minkowski convolutional neural networks.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting 4d spatio-temporal convnets: Minkowski convolutional neural networks

Reference 9

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Observation d44fe46a-7bb6-4a18-8263-8c2ead1bd291 · outbound

This paper cites MMDetection3D: Open- MMLab next-generation platform for general 3D object detection.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting MMDetection3D: Open- MMLab next-generation platform for general 3D object detection

Reference 10

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Observation fcb933eb-c593-4726-ba56-7e6fd17f734b · outbound

This paper cites Scannet: Richly-annotated 3d reconstructions of indoor scenes.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Scannet: Richly-annotated 3d reconstructions of indoor scenes

Reference 11

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Observation 813ae369-a2a8-4b6b-b832-706d0cad5a72 · outbound

This paper cites Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Autoencoders as Cross-Modal Teachers: Can Pretrained 2D Image Transformers Help 3D Representation Learning?

Reference 12

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Observation 7fc886cc-8c25-4d72-992a-8f8831c73762 · outbound

This paper cites Interpretable3d: An ad-hoc interpretable classifier for 3d point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Interpretable3d: An ad-hoc interpretable classifier for 3d point clouds

Reference 13

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Observation 2c5bd683-dedb-4bf3-b63b-4e3642912760 · outbound

This paper cites Shape2scene: 3d scene representation learning through pre- training on shape data.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Shape2scene: 3d scene representation learning through pre- training on shape data

Reference 14

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Observation 6e9ae006-c653-4351-91f4-22933ba4fa39 · outbound

This paper cites Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Mamba3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model

Reference 15

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Observation ca73e212-e73b-463e-8f87-17b09ee7440f · outbound

This paper cites Momentum contrast for unsupervised visual rep- resentation learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Momentum contrast for unsupervised visual rep- resentation learning

Reference 16

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Observation 336d8ec6-eb2c-44bc-ad8e-1ca26452a227 · outbound

This paper cites Masked autoencoders are scalable vision learners.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Masked autoencoders are scalable vision learners

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.230586Z

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Observation de72d34a-ceea-49c3-87b8-e983c4b3610e · outbound

This paper cites Exploring data-efficient 3d scene understanding with contrastive scene contexts.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Exploring data-efficient 3d scene understanding with contrastive scene contexts

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.217090Z

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Observation 690ec85d-bc02-477b-bf91-4bc853867266 · outbound

This paper cites Ponder: Point cloud pre-training via neural rendering.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Ponder: Point cloud pre-training via neural rendering

Reference 19

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Observation 7b213304-afa7-4cce-8f5e-0586a3417831 · outbound

This paper cites Spatio-temporal self-supervised representation learning for 3d point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Spatio-temporal self-supervised representation learning for 3d point clouds

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.190057Z

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Observation bcaeb34e-87c7-49c6-a5be-47746ab23648 · outbound

This paper cites Pointgroup: Dual-set point grouping for 3d instance segmentation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointgroup: Dual-set point grouping for 3d instance segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.176500Z

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Observation 4b40cee4-3252-4c9b-811d-44fefd9e7136 · outbound

This paper cites 3d gaussian splatting for real-time radiance field rendering.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting 3d gaussian splatting for real-time radiance field rendering

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.162943Z

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Observation 4905568b-9df1-4c3c-bbb3-8f85b8dc49b7 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Adam: A Method for Stochastic Optimization

Reference 23

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Observation 34b29a23-ad1c-4d8e-a483-b47064f0bc22 · outbound

This paper cites Oneformer3d: One transformer for unified point cloud segmentation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Oneformer3d: One transformer for unified point cloud segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.149512Z

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Observation 736f8bc0-ba75-4f1f-86f4-33f6176138d3 · outbound

This paper cites Stratified trans- former for 3d point cloud segmentation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Stratified trans- former for 3d point cloud segmentation

Reference 25

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Observation a00ea9e6-c194-489c-a110-cfab6ccf3893 · outbound

This paper cites Masked discrimina- tion for self-supervised learning on point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Masked discrimina- tion for self-supervised learning on point clouds

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.120191Z

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Observation a286fa25-e7be-4fde-acfb-b378419c2605 · outbound

This paper cites Regress before construct: Regress autoen- coder for point cloud self-supervised learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Regress before construct: Regress autoen- coder for point cloud self-supervised learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:10.105636Z

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Observation 6086aa3b-6674-4398-8601-a1a23161999e · outbound

This paper cites Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointclustering: Unsupervised point cloud pre-training using transformation invariance in clustering

Reference 28

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Observation 1851bfc1-4e3a-47cc-a7ff-50e24db2e2d4 · outbound

This paper cites Decoupled Weight Decay Regularization.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Decoupled Weight Decay Regularization

Reference 29

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Observation 7a230c0d-01a6-444a-be7f-48efb62e43a2 · outbound

This paper cites Unified-io: A unified model for vision, language, and multi-modal tasks.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Unified-io: A unified model for vision, language, and multi-modal tasks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.973132Z

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Observation 0c4dde46-4e09-43fa-8441-35d24d55b134 · outbound

This paper cites Re- thinking network design and local geometry in point cloud: A simple residual mlp framework.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Re- thinking network design and local geometry in point cloud: A simple residual mlp framework

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.959450Z

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Observation 093f9870-b2d9-4be7-a1ac-711699da62b5 · outbound

This paper cites Nerf: Representing scenes as neural radiance fields for view syn- thesis.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Nerf: Representing scenes as neural radiance fields for view syn- thesis

Reference 32

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Observation 473268ae-5636-4c3b-9f0c-8a94d5ebb8c8 · outbound

This paper cites Masked autoencoders for point cloud self-supervised learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Masked autoencoders for point cloud self-supervised learning

Reference 33

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Observation df674f62-7a3b-4611-884b-40e3dbaf3c04 · outbound

This paper cites Self-positioning point-based transformer for point cloud understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Self-positioning point-based transformer for point cloud understanding

Reference 34

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Observation 983c88f9-add4-4849-9b3b-b2e9352cb0d8 · outbound

This paper cites Oa-cnns: Omni- adaptive sparse cnns for 3d semantic segmentation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Oa-cnns: Omni- adaptive sparse cnns for 3d semantic segmentation

Reference 35

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Observation bc5e37ce-9f55-4408-8219-977d8df5af9c · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.894465Z

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Observation 97d014d6-f677-4e58-a323-c0f25a8fd0a7 · outbound

This paper cites Point- net++ deep hierarchical feature learning on point sets in a metric space.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point- net++ deep hierarchical feature learning on point sets in a metric space

Reference 37

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Observation 26730baa-6874-4baf-be99-319e3811a3fe · outbound

This paper cites Deep hough voting for 3d object detection in point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Deep hough voting for 3d object detection in point clouds

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.866805Z

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Observation 2475f84b-f42f-4cfd-af8c-81ee4e47d689 · outbound

This paper cites Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Contrast with Reconstruct: Contrastive 3D Representation Learning Guided by Generative Pretraining

Reference 39

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Observation f2fb4517-db85-412f-ad6b-96fd57bbedbe · outbound

This paper cites Vpp: Efficient conditional 3d generation via voxel-point pro- gressive representation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Vpp: Efficient conditional 3d generation via voxel-point pro- gressive representation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.852936Z

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Observation 9b88e7b1-f3f7-4e75-b822-dd27c8cf7aaa · outbound

This paper cites Shapellm: Universal 3d object understanding for embodied interaction.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Shapellm: Universal 3d object understanding for embodied interaction

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.839186Z

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Observation 70b74df7-61e8-4d38-8ec1-95fcb07745cd · outbound

This paper cites Pointnext: Revisiting pointnet++ with improved training and scaling strategies.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointnext: Revisiting pointnet++ with improved training and scaling strategies

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.825285Z

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Observation 269f482f-d420-4034-858d-65acc1bed702 · outbound

This paper cites Randomrooms: Unsupervised pre- training from synthetic shapes and randomized layouts for 3d object detection.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Randomrooms: Unsupervised pre- training from synthetic shapes and randomized layouts for 3d object detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.812077Z

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Observation 03b4733f-a91b-4ee2-b24f-3c76323f2421 · outbound

This paper cites Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Bringing Masked Autoencoders Explicit Contrastive Properties for Point Cloud Self-Supervised Learning

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:41:04.839722Z

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Observation 044878b1-a8c3-41f2-8503-587f74f80e95 · outbound

This paper cites High-resolution image syn- thesis with latent diffusion models.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting High-resolution image syn- thesis with latent diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.798249Z

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Observation 9b55cec0-acf0-42ea-9d54-32ad5968488f · outbound

This paper cites Language- grounded indoor 3d semantic segmentation in the wild.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Language- grounded indoor 3d semantic segmentation in the wild

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.784726Z

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Observation 057ebd25-c3a0-4be2-ad8a-b83b69b96557 · outbound

This paper cites Language- grounded indoor 3d semantic segmentation in the wild.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Language- grounded indoor 3d semantic segmentation in the wild

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.770338Z

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Observation a07a932e-5254-4251-8c86-8d3beacf3539 · outbound

This paper cites Splatter image: Ultra-fast single-view 3d recon- struction.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Splatter image: Ultra-fast single-view 3d recon- struction

Reference 48

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Observation ef06ca7f-f132-4f6a-98c9-2be96f248253 · outbound

This paper cites Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.742381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e4ee0c28-c0f0-4556-95b8-8a4d6797ce35 · outbound

This paper cites Kpconvx: Modernizing kernel point convolution with kernel attention.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Kpconvx: Modernizing kernel point convolution with kernel attention

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.728424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 76e52a76-d984-4836-a2a5-fe16dd444c7b · outbound

This paper cites Revisiting point cloud classification: A new benchmark dataset and classifi- cation model on real-world data.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Revisiting point cloud classification: A new benchmark dataset and classifi- cation model on real-world data

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.715139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a78d4f8c-d279-4811-a40a-869d9c071885 · outbound

This paper cites Attention is all you need.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Attention is all you need

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.701755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 364d2140-e043-4359-b163-12afc3fc0db3 · outbound

This paper cites Groupcontrast: Semantic-aware self-supervised representation learning for 3d understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Groupcontrast: Semantic-aware self-supervised representation learning for 3d understanding

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.630976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3303301f-6ec3-42ab-9553-c9abdf42a4fd · outbound

This paper cites GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting GPSFormer: A Global Perception and Local Structure Fitting-based Transformer for Point Cloud Understanding

Reference 54

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:41:04.734480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e22a1e23-a620-45c4-a4cb-f792dc1a31a4 · outbound

This paper cites Unsupervised point cloud pre-training via occlusion completion.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Unsupervised point cloud pre-training via occlusion completion

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.407815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ef3c2a79-f337-4f80-81b2-40a77096f5e4 · outbound

This paper cites Beyond first impressions: Integrating joint multi-modal cues for comprehensive 3d representation.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Beyond first impressions: Integrating joint multi-modal cues for comprehensive 3d representation

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:09.142882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3970a2bb-d097-4f1d-9b16-050062aafdfa · outbound

This paper cites Octformer: Octree-based transformers for 3d point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Octformer: Octree-based transformers for 3d point clouds

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.885798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dac5943d-6b95-4261-a059-af6d4af3ea00 · outbound

This paper cites Image as a foreign language: Beit pretraining for vision and vision- language tasks.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Image as a foreign language: Beit pretraining for vision and vision- language tasks

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.747171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9fccd007-3bd7-43f5-98d0-9377eae12367 · outbound

This paper cites Dynamic graph cnn for learning on point clouds.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Dynamic graph cnn for learning on point clouds

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.544572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a63f46d3-a893-4efb-ab47-f08887ae2ff4 · outbound

This paper cites Take-a-photo: 3d-to-2d generative pre-training of point cloud models.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Take-a-photo: 3d-to-2d generative pre-training of point cloud models

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.402407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6fd7d773-97be-4fab-82b9-8c1da5ce92af · outbound

This paper cites Point transformer v2: Grouped vector atten- tion and partition-based pooling.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point transformer v2: Grouped vector atten- tion and partition-based pooling

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.209413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 47c50c2c-3f66-4ca9-ac45-6c0d0f7b2ff9 · outbound

This paper cites Masked scene contrast: A scalable framework for unsuper- vised 3d representation learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Masked scene contrast: A scalable framework for unsuper- vised 3d representation learning

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:08.043908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 96aa77d7-b6eb-41f5-936e-a270000fe9ce · outbound

This paper cites Point transformer v3: Simpler faster stronger.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point transformer v3: Simpler faster stronger

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.870083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 896e2254-d1fb-4969-b675-c23fed0802b2 · outbound

This paper cites Towards large- scale 3d representation learning with multi-dataset point prompt training.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Towards large- scale 3d representation learning with multi-dataset point prompt training

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.730369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 16caaeab-d06d-494b-9e34-832cb66f9a11 · outbound

This paper cites Pointcontrast: Unsupervised pre- training for 3d point cloud understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.569658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 87d261c1-0ca4-4c7f-b947-cf59012a6b21 · outbound

This paper cites Disn: Deep implicit surface network for high-quality single-view 3d reconstruction.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Disn: Deep implicit surface network for high-quality single-view 3d reconstruction

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.446458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 71d73efe-64e4-446a-a618-8925c1b1d61d · outbound

This paper cites Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.267670Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 185cbedc-8a03-4658-ba68-51e5477137e9 · outbound

This paper cites Ulip-2: Towards scal- able multimodal pre-training for 3d understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Ulip-2: Towards scal- able multimodal pre-training for 3d understanding

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:02.891924Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c00677fe-b7ce-4701-ba60-fac2d4e360f3 · outbound

This paper cites Point cloud pre- training with natural 3d structures.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point cloud pre- training with natural 3d structures

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:07.136552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3c4ca1dd-631e-44a7-b9c0-21c0ce80aa10 · outbound

This paper cites Implicit autoencoder for point-cloud self-supervised representation learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Implicit autoencoder for point-cloud self-supervised representation learning

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:06.945164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9743ce23-6a1b-4d17-bb02-053fe55bdd20 · outbound

This paper cites Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:03.183247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ebe5472-404b-42b4-a4b8-7f164ce1a028 · outbound

This paper cites A scalable active framework for region annotation in 3d shape collections.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting A scalable active framework for region annotation in 3d shape collections

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:06.772966Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a6e1c4b0-adbe-4b56-9bc8-e91d25487876 · outbound

This paper cites Point-bert: Pre-training 3d point cloud transformers with masked point modeling.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point-bert: Pre-training 3d point cloud transformers with masked point modeling

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:06.634628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation badfed17-6c03-42bc-b1a9-b100236ec4a9 · outbound

This paper cites Towards compact 3d representations via point feature enhancement masked au- toencoders.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Towards compact 3d representations via point feature enhancement masked au- toencoders

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:06.470542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c13be060-6ee0-46bd-8d13-775e78418a79 · outbound

This paper cites Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:06.208715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 1f3fe4a7-d9bc-4446-b7b7-db66092d5c6a · outbound

This paper cites Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Learning 3d representations from 2d pre-trained models via image-to-point masked autoencoders

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:05.966015Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bf956329-c03f-424f-a247-4d7291452bf4 · outbound

This paper cites Point Cloud Mamba: Point Cloud Learning via State Space Model.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point Cloud Mamba: Point Cloud Learning via State Space Model

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:03.813528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a03f485-6c03-407a-9b63-89e9dbb390ea · outbound

This paper cites PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:03.870296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 073e77fb-ad37-4dd2-97d6-dd1e769597b4 · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:03.961669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27ad4fa9-7ece-4c7b-a31c-6e40ec1d5176 · outbound

This paper cites Self-supervised pretraining of 3d features on any point-cloud.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Self-supervised pretraining of 3d features on any point-cloud

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:05.622540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 059590d5-f61f-466a-b7af-de8de77e3977 · outbound

This paper cites Point transformer.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point transformer

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:05.507360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ada1d011-2501-4276-bab6-7b4d2893d02d · outbound

This paper cites Point cloud pre-training with diffusion models.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Point cloud pre-training with diffusion models

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:05.362789Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 450fda01-94db-4755-b9e1-a6115229ef74 · outbound

This paper cites Uni3D: Exploring Unified 3D Representation at Scale.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Uni3D: Exploring Unified 3D Representation at Scale

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:04.314408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fe3aae7-8eb6-4281-9f19-91c6961eda35 · outbound

This paper cites PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-07T04:41:04.397654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70247d32-878b-4580-8048-4141c12d1d2f · outbound

This paper cites Uni-perceiver: Pre- training unified architecture for generic perception for zero- shot and few-shot tasks.

UniPre3D: Unified Pre-training of 3D Point Cloud Models with Cross-Modal Gaussian Splatting Uni-perceiver: Pre- training unified architecture for generic perception for zero- shot and few-shot tasks

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:41:05.123244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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