Generative Data Augmentation for Object Point Cloud Segmentation | Paper Citation Record

Observation 908a32c3-5df2-44d1-99cc-19623003f501 · outbound

This paper cites Synthetic Data from Diffusion Models Improves ImageNet Classification.

Generative Data Augmentation for Object Point Cloud Segmentation Synthetic Data from Diffusion Models Improves ImageNet Classification

Reference 1

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Observation 5324f8ea-2549-4c04-a5cf-696456d5c4bf · outbound

This paper cites Segmentor: Obtaining efficient operating room semantics through temporal propa- gation.

Generative Data Augmentation for Object Point Cloud Segmentation Segmentor: Obtaining efficient operating room semantics through temporal propa- gation

Reference 2

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Observation b7085a37-dbbd-4880-9be9-2d4819e0af55 · outbound

This paper cites Shape self-correction for unsupervised point cloud understanding.

Generative Data Augmentation for Object Point Cloud Segmentation Shape self-correction for unsupervised point cloud understanding

Reference 3

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Observation df9267f8-af71-44b2-956e-46c263eaa0f2 · outbound

This paper cites Bae-net: Branched autoencoder for shape co-segmentation.

Generative Data Augmentation for Object Point Cloud Segmentation Bae-net: Branched autoencoder for shape co-segmentation

Reference 4

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Observation 1c05b64b-024b-4fe3-8a1d-31654c2996ed · outbound

This paper cites Sspc-net: Semi-supervised semantic 3d point cloud segmentation net- work.

Generative Data Augmentation for Object Point Cloud Segmentation Sspc-net: Semi-supervised semantic 3d point cloud segmentation net- work

Reference 5

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Observation 0a79daa3-9893-4987-aaeb-657d3c7a6c7d · outbound

This paper cites ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation.

Generative Data Augmentation for Object Point Cloud Segmentation ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation

Reference 6

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Observation d5bef57e-0ae8-48aa-9d60-c92596fcc203 · outbound

This paper cites Generative adversarial networks.

Generative Data Augmentation for Object Point Cloud Segmentation Generative adversarial networks

Reference 7

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Observation 44cfbef6-e0c3-4b05-8788-a4de1cfac49f · outbound

This paper cites Deep residual learning for image recognition.

Generative Data Augmentation for Object Point Cloud Segmentation Deep residual learning for image recognition

Reference 8

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Observation 67b64720-6b74-4e83-ae1f-fbab2eda55dd · outbound

This paper cites Is synthetic data from generative models ready for image recognition?.

Generative Data Augmentation for Object Point Cloud Segmentation Is synthetic data from generative models ready for image recognition?

Reference 9

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Observation a26c59db-5215-4473-a195-95b84b70498b · outbound

This paper cites Denoising dif- fusion probabilistic models.

Generative Data Augmentation for Object Point Cloud Segmentation Denoising dif- fusion probabilistic models

Reference 10

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Observation b5af4a7d-ee3c-4e1c-9050-d1e563a3579e · outbound

This paper cites Squeeze-and-excitation net- works.

Generative Data Augmentation for Object Point Cloud Segmentation Squeeze-and-excitation net- works

Reference 11

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Observation 9d23dfb7-2077-44af-a2c6-6dcf1a58c57d · outbound

This paper cites Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds.

Generative Data Augmentation for Object Point Cloud Segmentation Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds

Reference 12

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Observation ea65a137-d066-4abd-9307-2dcda95e5b61 · outbound

This paper cites Lpcg: A self-conditional architecture for labeled point cloud generation.

Generative Data Augmentation for Object Point Cloud Segmentation Lpcg: A self-conditional architecture for labeled point cloud generation

Reference 13

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Observation 5b6c3062-b99d-4402-9d1d-73ccaebfa60d · outbound

This paper cites Guided point contrastive learn- ing for semi-supervised point cloud semantic segmentation.

Generative Data Augmentation for Object Point Cloud Segmentation Guided point contrastive learn- ing for semi-supervised point cloud semantic segmentation

Reference 14

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Observation 398c414f-e97c-4f39-a97c-120c0e9f9575 · outbound

This paper cites Elucidating the design space of diffusion-based generative models.

Generative Data Augmentation for Object Point Cloud Segmentation Elucidating the design space of diffusion-based generative models

Reference 15

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Observation 699dc281-0c32-4650-9cdd-6a9b5440e1b2 · outbound

This paper cites Semi-supervised learning with deep gen- erative models.

Generative Data Augmentation for Object Point Cloud Segmentation Semi-supervised learning with deep gen- erative models

Reference 16

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Observation 52af5587-5f78-4049-8b39-2e87dc9eeaf9 · outbound

This paper cites 3d- vfield: Adversarial augmentation of point clouds for domain generalization in 3d object detection.

Generative Data Augmentation for Object Point Cloud Segmentation 3d- vfield: Adversarial augmentation of point clouds for domain generalization in 3d object detection

Reference 17

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Observation 30f14229-29cd-4035-a690-a7623bd4000e · outbound

This paper cites 3d adversarial augmentations for robust out-of-domain predictions.

Generative Data Augmentation for Object Point Cloud Segmentation 3d adversarial augmentations for robust out-of-domain predictions

Reference 18

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Observation 496b62e7-70ca-43fe-9017-30a496ac3d17 · outbound

This paper cites Pseudoaugment: Learning to use unla- beled data for data augmentation in point clouds.

Generative Data Augmentation for Object Point Cloud Segmentation Pseudoaugment: Learning to use unla- beled data for data augmentation in point clouds

Reference 19

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Observation 8f567f78-6332-4be0-90f1-d69446931c18 · outbound

This paper cites Less: Label-efficient semantic segmentation for lidar point clouds.

Generative Data Augmentation for Object Point Cloud Segmentation Less: Label-efficient semantic segmentation for lidar point clouds

Reference 20

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Observation 9df54311-43e5-45b6-b6ef-b041a9aaa7aa · outbound

This paper cites Point- voxel cnn for efficient 3d deep learning.

Generative Data Augmentation for Object Point Cloud Segmentation Point- voxel cnn for efficient 3d deep learning

Reference 21

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Observation 0deda63f-a88e-4b76-80b5-1808d67a287a · outbound

This paper cites One thing one click: A self-training approach for weakly supervised 3d semantic segmentation.

Generative Data Augmentation for Object Point Cloud Segmentation One thing one click: A self-training approach for weakly supervised 3d semantic segmentation

Reference 22

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Observation 57e43df7-1af3-460d-ac56-51512157a5d8 · outbound

This paper cites Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data.

Generative Data Augmentation for Object Point Cloud Segmentation Project to adapt: Domain adaptation for depth completion from noisy and sparse sensor data

Reference 23

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Observation c7b34dc1-b7e3-4311-b0dd-ad21d57e14c1 · outbound

This paper cites Diffusion probabilistic models for 3d point cloud generation.

Generative Data Augmentation for Object Point Cloud Segmentation Diffusion probabilistic models for 3d point cloud generation

Reference 24

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Observation ccba5073-4955-4fc6-ad5f-9ba78a417a39 · outbound

This paper cites SDEdit: Guided image synthesis and editing with stochastic differential equa- tions.

Generative Data Augmentation for Object Point Cloud Segmentation SDEdit: Guided image synthesis and editing with stochastic differential equa- tions

Reference 25

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Observation 9c26267f-6a4d-4468-88d0-1bbd49959eca · outbound

This paper cites Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 9 3d object understanding.

Generative Data Augmentation for Object Point Cloud Segmentation Partnet: A large- scale benchmark for fine-grained and hierarchical part-level 9 3d object understanding

Reference 26

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Observation e263e3c0-cda1-40c8-bd43-8ebc139c3ffe · outbound

This paper cites An Overview of Deep Semi-Supervised Learning.

Generative Data Augmentation for Object Point Cloud Segmentation An Overview of Deep Semi-Supervised Learning

Reference 27

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Observation 7326cf50-4fb8-4543-8c06-b52a242a3912 · outbound

This paper cites 3d part segmentation on shapenet-part.

Generative Data Augmentation for Object Point Cloud Segmentation 3d part segmentation on shapenet-part

Reference 28

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Observation 5167ad08-5464-49a4-93cd-1d31de8a0881 · outbound

This paper cites Lee, Si Hyeon Kim, Yunyang Xiong, and Hyunwoo J.

Generative Data Augmentation for Object Point Cloud Segmentation Lee, Si Hyeon Kim, Yunyang Xiong, and Hyunwoo J

Reference 29

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Observation d226be92-e089-4756-ba28-8b7dd0db1816 · outbound

This paper cites Qi, Hao Su, Kaichun Mo, and Leonidas J.

Generative Data Augmentation for Object Point Cloud Segmentation Qi, Hao Su, Kaichun Mo, and Leonidas J

Reference 30

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Observation e1faae5d-ece3-408c-8088-80cd6f299f8b · outbound

This paper cites an unresolved cited work.

Generative Data Augmentation for Object Point Cloud Segmentation Unresolved cited work

Reference 31

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Observation 7e4557d5-f599-4058-bda5-6eb2e0b11303 · outbound

This paper cites Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining.

Generative Data Augmentation for Object Point Cloud Segmentation Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

Reference 32

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Observation 656bc860-7f89-4295-84cb-d77cbf58196a · outbound

This paper cites Bringing masked autoencoders explicit con- trastive properties for point cloud self-supervised learning.

Generative Data Augmentation for Object Point Cloud Segmentation Bringing masked autoencoders explicit con- trastive properties for point cloud self-supervised learning

Reference 33

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Observation b07a29d6-faf2-4a7f-a672-93be99def430 · outbound

This paper cites DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis.

Generative Data Augmentation for Object Point Cloud Segmentation DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis

Reference 34

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Observation c126979a-2156-42ea-944f-99c80c02e752 · outbound

This paper cites Effective Data Augmentation With Diffusion Models.

Generative Data Augmentation for Object Point Cloud Segmentation Effective Data Augmentation With Diffusion Models

Reference 35

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Observation e84e47d6-53ca-4c49-8ed3-de43b8b0ce14 · outbound

This paper cites Few-shot learning of part-specific probability space for 3d shape segmentation.

Generative Data Augmentation for Object Point Cloud Segmentation Few-shot learning of part-specific probability space for 3d shape segmentation

Reference 36

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Observation abe37a4a-9b10-44bd-8790-9734c4a7bb58 · outbound

This paper cites Group normalization.

Generative Data Augmentation for Object Point Cloud Segmentation Group normalization

Reference 37

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Observation 6fb8c125-4b94-4199-8dc0-62c56e8527e3 · outbound

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

Generative Data Augmentation for Object Point Cloud Segmentation Pointcontrast: Unsupervised pre- training for 3d point cloud understanding

Reference 38

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Observation 477accd8-cef6-4ba3-bcb9-184bd1b766e0 · outbound

This paper cites Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels.

Generative Data Augmentation for Object Point Cloud Segmentation Weakly supervised semantic point cloud segmentation: Towards 10x fewer labels

Reference 39

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Observation f8f26687-bb7f-4562-a1d6-f3bfa23aa6c1 · outbound

This paper cites An mil-derived transformer for weakly supervised point cloud segmentation.

Generative Data Augmentation for Object Point Cloud Segmentation An mil-derived transformer for weakly supervised point cloud segmentation

Reference 40

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Observation 172de9f2-36db-4179-b201-d96f29e6c033 · outbound

This paper cites Intra: 3d intracranial aneurysm dataset for deep learning.

Generative Data Augmentation for Object Point Cloud Segmentation Intra: 3d intracranial aneurysm dataset for deep learning

Reference 41

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Observation e028d389-f1ff-4a28-a8c1-369ac4987b78 · outbound

This paper cites Yi, Vladimir G.

Generative Data Augmentation for Object Point Cloud Segmentation Yi, Vladimir G

Reference 42

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Observation 444c5e43-1ea0-4ead-a32e-1175826219e8 · outbound

This paper cites Diffusion models and semi-supervised learners benefit mutually with few labels.

Generative Data Augmentation for Object Point Cloud Segmentation Diffusion models and semi-supervised learners benefit mutually with few labels

Reference 43

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Observation e62dc691-4bcc-4685-8cc3-ed6d96e1760d · outbound

This paper cites LegoNet: A Fast and Exact Unlearning Architecture.

Generative Data Augmentation for Object Point Cloud Segmentation LegoNet: A Fast and Exact Unlearning Architecture

Reference 44

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Observation 274834e7-a1f2-44df-95e4-5d0d843daf26 · outbound

This paper cites Lion: Latent point diffusion models for 3d shape generation.

Generative Data Augmentation for Object Point Cloud Segmentation Lion: Latent point diffusion models for 3d shape generation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:52.430124Z

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Observation 894c3bba-770b-4d7a-94f1-1f38eb7b50b7 · outbound

This paper cites Echoscene: Indoor scene generation via information echo over scene graph diffusion.

Generative Data Augmentation for Object Point Cloud Segmentation Echoscene: Indoor scene generation via information echo over scene graph diffusion

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:52.275668Z

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Observation d289a3ca-b72f-4f31-83e1-e3047d2f9940 · outbound

This paper cites Commonscenes: Generating commonsense 3d indoor scenes with scene graphs.

Generative Data Augmentation for Object Point Cloud Segmentation Commonscenes: Generating commonsense 3d indoor scenes with scene graphs

Reference 47

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Observation b076b1b9-c5ea-4147-88e2-ed76f49a0402 · outbound

This paper cites Point transformer.

Generative Data Augmentation for Object Point Cloud Segmentation Point transformer

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:52.014368Z

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Observation 7c5d0faf-da29-4f93-99a1-a22f5c8368e0 · outbound

This paper cites Toward understanding generative data augmentation.

Generative Data Augmentation for Object Point Cloud Segmentation Toward understanding generative data augmentation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.876397Z

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Observation 723fde06-2ae7-4647-b928-ba8d16f571e2 · outbound

This paper cites 3d shape generation and completion through point-voxel diffusion.

Generative Data Augmentation for Object Point Cloud Segmentation 3d shape generation and completion through point-voxel diffusion

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.756237Z

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Observation c9d64b72-df2a-4da3-94c7-3c951af470a1 · outbound

This paper cites Ipcc-tp: Utilizing incre- mental pearson correlation coefficient for joint multi-agent trajectory prediction.

Generative Data Augmentation for Object Point Cloud Segmentation Ipcc-tp: Utilizing incre- mental pearson correlation coefficient for joint multi-agent trajectory prediction

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.599233Z

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Observation 4455088e-bc5a-46be-9ef8-b1496554932c · outbound

This paper cites Multi-vehicle trajectory prediction and control at intersections using state 10 and intention information.

Generative Data Augmentation for Object Point Cloud Segmentation Multi-vehicle trajectory prediction and control at intersections using state 10 and intention information

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.470016Z

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Observation f2bdfefe-d916-43f8-b83f-41933aed4442 · outbound

This paper cites Sealion: Semantic part-aware latent point diffusion models for 3d generation.

Generative Data Augmentation for Object Point Cloud Segmentation Sealion: Semantic part-aware latent point diffusion models for 3d generation

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.306536Z

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Observation 9adb9b31-9179-43b9-9c45-cd5e8268fddc · outbound

This paper cites Spiral: Semantic- aware progressive lidar scene generation.

Generative Data Augmentation for Object Point Cloud Segmentation Spiral: Semantic- aware progressive lidar scene generation

Reference 54

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:44:49.270950Z

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Observation 85ba70a6-979f-482d-86ad-2ff0a146040e · outbound

This paper cites Preliminaries.

Generative Data Augmentation for Object Point Cloud Segmentation Preliminaries

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.175444Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 97e7ffda-78a6-4651-9c4a-2d0de18de64f · outbound

This paper cites Experimental Settings.

Generative Data Augmentation for Object Point Cloud Segmentation Experimental Settings

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:51.025101Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bcf942e6-16f9-48e2-8c0f-b40aeb8fdecc · outbound

This paper cites More Experimental Results 12 A.1.

Generative Data Augmentation for Object Point Cloud Segmentation More Experimental Results 12 A.1

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:50.874939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7fbdc3cc-cc9d-4d28-917c-7df194b60347 · outbound

This paper cites an unresolved cited work.

Generative Data Augmentation for Object Point Cloud Segmentation Unresolved cited work

Reference 58

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:44:50.717018Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d4477e71-3a7e-4b44-8058-8315d5ef6f71 · outbound

This paper cites GDA for PointNet [30], PointNet++ [31], and SPoTr [29] on IntrA [41] dataset.

Generative Data Augmentation for Object Point Cloud Segmentation GDA for PointNet [30], PointNet++ [31], and SPoTr [29] on IntrA [41] dataset

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:50.580796Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c546acb1-e0b6-47c5-9d7f-4dd113aa50ba · outbound

This paper cites Although the level 2 samples contain artifacts of jittering points or non-uniformly distributed points, it gener- ally maintains a reasonable shape and segmentation labels.

Generative Data Augmentation for Object Point Cloud Segmentation Although the level 2 samples contain artifacts of jittering points or non-uniformly distributed points, it gener- ally maintains a reasonable shape and segmentation labels

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:50.414625Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea2a8dd9-e611-4d55-a500-1361ea2d5af1 · outbound

This paper cites an unresolved cited work.

Generative Data Augmentation for Object Point Cloud Segmentation Unresolved cited work

Reference 61

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:44:50.267074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 50903e07-915b-4907-9b1b-eab972c1552f · outbound

This paper cites an unresolved cited work.

Generative Data Augmentation for Object Point Cloud Segmentation Unresolved cited work

Reference 62

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:44:50.126055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3707b7c4-e08a-4a79-af07-3d9438fbdff1 · outbound

This paper cites The segmentation results on cars and airplanes from ShapeNetPart [42] are demonstrated in Fig.

Generative Data Augmentation for Object Point Cloud Segmentation The segmentation results on cars and airplanes from ShapeNetPart [42] are demonstrated in Fig

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:44:49.968485Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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