Vanilla ViT for Automotive Point Cloud Semantic Segmentation | Paper Citation Record

Observation 5d9e3acb-4f01-4853-957a-935a527c6c20 · outbound

This paper cites RangeViT: Towards Vision Transformers for 3D Se- mantic Segmentation in Autonomous Driving.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation RangeViT: Towards Vision Transformers for 3D Se- mantic Segmentation in Autonomous Driving

Reference 1

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Observation 342e6dba-ba0c-4032-99fa-4438c6d53a2b · outbound

This paper cites Vivit: A video vision transformer.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Vivit: A video vision transformer

Reference 2

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Observation 359e7da7-8a82-400b-95d7-9a6a4b355e03 · outbound

This paper cites Behley, M.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Behley, M

Reference 3

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Observation a82a180f-dd33-4881-b0b6-29b0330e2e50 · outbound

This paper cites Fkaconv: Feature-kernel alignment for point cloud convolution.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Fkaconv: Feature-kernel alignment for point cloud convolution

Reference 4

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Observation 426949bf-b63a-4038-8aa4-c3e745892f24 · outbound

This paper cites Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krish- nan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krish- nan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom

Reference 5

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Observation d643f13d-db8c-4be9-befc-190c981e6d79 · outbound

This paper cites Emerging properties in self-supervised vision transformers.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Emerging properties in self-supervised vision transformers

Reference 6

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Observation ba9663ca-201d-4e20-9965-25f10baa4c05 · outbound

This paper cites (AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Se- mantic Segmentation Network.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation (AF)2-S3Net: Attentive Feature Fusion With Adaptive Feature Selection for Sparse Se- mantic Segmentation Network

Reference 7

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Observation ce904e4d-2b73-4a17-9326-309b858d8d78 · outbound

This paper cites PointMixer: MLP- Mixer for Point Cloud Understanding.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PointMixer: MLP- Mixer for Point Cloud Understanding

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 279f524b-57bb-49a2-8484-a6dc0688f988 · outbound

This paper cites 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation 4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 3830651c-5603-467a-8a26-4f017de0620b · outbound

This paper cites SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation f1d499ff-42af-4323-9498-5154b4459391 · outbound

This paper cites Scaling vision trans- formers to 22 billion parameters.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Scaling vision trans- formers to 22 billion parameters

Reference 11

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Observation c144bea9-9428-4153-bef7-41576fd399a6 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation An image is worth 16x16 words: Transformers for image recognition at scale

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 6e0a711a-d882-4bcf-9851-b7687a4c1dbc · outbound

This paper cites TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond in- ceptiOn module.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond in- ceptiOn module

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 1f230ac2-e242-4201-aaec-2785f9371ea4 · outbound

This paper cites AST: Audio Spectrogram Transformer.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation AST: Audio Spectrogram Transformer

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:47.102000Z

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Observation eb53c3cd-2da0-45d3-a798-d0c29275a4b9 · outbound

This paper cites Rotary position embedding for vision trans- former.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Rotary position embedding for vision trans- former

Reference 15

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Observation eaa0458c-6b37-43ac-8fe9-2287d913e300 · outbound

This paper cites Point-to-V oxel Knowledge Dis- tillation for LiDAR Semantic Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Point-to-V oxel Knowledge Dis- tillation for LiDAR Semantic Segmentation

Reference 16

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Observation b1571385-9e9e-4ab2-a2f3-bdbe54b9172f · outbound

This paper cites Deep networks with stochastic depth.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Deep networks with stochastic depth

Reference 17

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Observation 84bb0dbd-2ea7-4609-8334-a8898246957a · outbound

This paper cites Dino in the room: Leveraging 2d founda- tion models for 3d segmentation.CVPRW, 2025.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Dino in the room: Leveraging 2d founda- tion models for 3d segmentation.CVPRW, 2025

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 34084ba3-92fb-4d38-9bd0-89607282ad97 · outbound

This paper cites KPRNet: Improving projection-based LiDAR semantic segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation KPRNet: Improving projection-based LiDAR semantic segmentation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:47.096889Z

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Observation e5e6f95d-770a-45b7-a046-cf4522b78d79 · outbound

This paper cites Rethinking range view representation for lidar segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Rethinking range view representation for lidar segmentation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 9cc7d5f7-8391-4977-a509-4ef897f934ca · outbound

This paper cites Lasermix for semi-supervised lidar semantic seg- mentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Lasermix for semi-supervised lidar semantic seg- mentation

Reference 21

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Observation 4b81d156-6534-453b-bafb-d6c7c94c570d · outbound

This paper cites Stratified Transformer for 3D Point Cloud Segmenta- tion.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Stratified Transformer for 3D Point Cloud Segmenta- tion

Reference 22

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 317e36da-474f-45c2-9f65-c4b75267e4a9 · outbound

This paper cites Spherical Transformer for LiDAR-Based 3D Recognition.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Spherical Transformer for LiDAR-Based 3D Recognition

Reference 23

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 03a612a9-f695-490d-aab6-3508d5712d31 · outbound

This paper cites Large-scale point cloud semantic segmentation with superpoint graphs.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Large-scale point cloud semantic segmentation with superpoint graphs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 0afe1da4-e7aa-4be8-b77d-acb3c8d6d14f · outbound

This paper cites Self-distillation for robust lidar semantic segmentation in autonomous driving.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Self-distillation for robust lidar semantic segmentation in autonomous driving

Reference 25

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation e0c38081-ebc7-43a9-90b2-2bf8f7ea3d2f · outbound

This paper cites AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation AMVNet: Assertion-based Multi-View Fusion Network for LiDAR Semantic Segmentation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:42:47.099464Z

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Observation bb53ee2b-bc48-44c3-90c6-aaf9c5dcc2ec · outbound

This paper cites Flatformer: Flattened window atten- tion for efficient point cloud transformer.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Flatformer: Flattened window atten- tion for efficient point cloud transformer

Reference 27

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 7b055b47-ff6a-4eb4-8530-88c1957a6bfb · outbound

This paper cites Decoupled weight decay regularization.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Decoupled weight decay regularization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation fc1a080d-9c29-4dfb-8b84-ace25829b665 · outbound

This paper cites Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework

Reference 29

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 5b7ed15c-b93f-419d-b067-eea0797a546f · outbound

This paper cites RangeNet ++: Fast and Accurate Li- DAR Semantic Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation RangeNet ++: Fast and Accurate Li- DAR Semantic Segmentation

Reference 30

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation d09a8ba2-ce7f-426d-af59-b3ce61cc098d · outbound

This paper cites Fast Point Transformer.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Fast Point Transformer

Reference 31

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 67941d85-95f2-4800-a3e8-ce91942a7c0b · outbound

This paper cites PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network.Expert Systems with Applications, 2023.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PCSCNet: Fast 3D semantic segmentation of LiDAR point cloud for autonomous car using point convolution and sparse convolution network.Expert Systems with Applications, 2023

Reference 32

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 84e6d91b-4f56-43e2-9b84-6278820da762 · outbound

This paper cites Using a waffle iron for automotive point cloud seman- tic segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Using a waffle iron for automotive point cloud seman- tic segmentation

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 58d23dc1-7f44-400c-ab5d-e8b33bd8bb11 · outbound

This paper cites Three pillars improving vi- sion foundation model distillation for lidar.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Three pillars improving vi- sion foundation model distillation for lidar

Reference 34

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 43829703-bd1c-409d-b4a2-7017dc625929 · outbound

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

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Qi, Hao Su, Kaichun Mo, and Leonidas J

Reference 35

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation fb81292a-bf19-466c-8fc8-c7e7e131e302 · outbound

This paper cites PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Reference 36

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 9042993f-b962-4f89-9657-bcd09fc97a7d · outbound

This paper cites PointNeXt: Revisit- ing PointNet++ with Improved Training and Scaling Strategies.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PointNeXt: Revisit- ing PointNet++ with Improved Training and Scaling Strategies

Reference 37

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation e31e654b-1aa3-4d05-bb17-a11e72874bc5 · outbound

This paper cites GFNet: Geometric Flow Network for 3D Point Cloud Seman- tic Segmentation.TMLR, 2022.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation GFNet: Geometric Flow Network for 3D Point Cloud Seman- tic Segmentation.TMLR, 2022

Reference 38

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation e7bc6461-9573-4a00-a8d5-783f141fe7ef · outbound

This paper cites Rist, David Schmidt, Markus Enzweiler, and Dariu M.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Rist, David Schmidt, Markus Enzweiler, and Dariu M

Reference 39

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation b9c02527-d558-49e1-b1db-97dc7802527f · outbound

This paper cites Efficient 3d semantic segmentation with superpoint transformer.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Efficient 3d semantic segmentation with superpoint transformer

Reference 40

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 1e00822f-d0ff-4c4d-946e-2f7fb919cce8 · outbound

This paper cites LMSCNet: Lightweight Multiscale 3D Se- mantic Completion.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation LMSCNet: Lightweight Multiscale 3D Se- mantic Completion

Reference 41

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation b8b4627b-4fca-416d-861c-c88ac2f5e217 · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.Neuro- computing, 2023.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Roformer: Enhanced transformer with rotary position embedding.Neuro- computing, 2023

Reference 42

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 6030a574-df71-4347-a4c1-3cf2769f47f7 · outbound

This paper cites Scalability in perception for au- tonomous driving: Waymo open dataset.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Scalability in perception for au- tonomous driving: Waymo open dataset

Reference 43

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation c7f87a89-1fda-4519-a6b3-4c653d5966a1 · outbound

This paper cites Searching effi- cient 3d architectures with sparse point-voxel convo- lution.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Searching effi- cient 3d architectures with sparse point-voxel convo- lution

Reference 44

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation a83cce55-ed51-442d-a883-bc340061dee9 · outbound

This paper cites Qi, Jean-Emmanuel De- schaud, Beatriz Marcotegui, Francois Goulette, and Leonidas J.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Qi, Jean-Emmanuel De- schaud, Beatriz Marcotegui, Francois Goulette, and Leonidas J

Reference 45

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation c5be9ec4-b0f3-4b3a-9609-def8fe76a0d4 · outbound

This paper cites MLP-Mixer: An all-MLP Architecture for Vision.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation MLP-Mixer: An all-MLP Architecture for Vision

Reference 46

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 3f452be7-84f0-4ba7-ac56-7a4f9a46fe40 · outbound

This paper cites Attention is all you need.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Attention is all you need

Reference 47

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 2c1c721d-9c20-45fc-b7a4-23053267d777 · outbound

This paper cites Dy- namic graph cnn for learning on point clouds.ACM Transactions On Graphics, 2019.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Dy- namic graph cnn for learning on point clouds.ACM Transactions On Graphics, 2019

Reference 48

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 480b83b7-4956-4d2a-9970-6a468b0ac836 · outbound

This paper cites Point transformer v3: Simpler faster stronger.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Point transformer v3: Simpler faster stronger

Reference 49

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Observation d8066383-2211-4dc9-bbfb-c97339ca5a40 · outbound

This paper cites Semi-supervised 3d object detec- tion with patchteacher and pillarmix.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Semi-supervised 3d object detec- tion with patchteacher and pillarmix

Reference 50

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 042b5f22-2be5-4686-a733-b4dfd00f574d · outbound

This paper cites PolarMix: A Gen- eral Data Augmentation Technique for LiDAR Point Clouds.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PolarMix: A Gen- eral Data Augmentation Technique for LiDAR Point Clouds

Reference 51

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 76d0be64-071c-4c5e-bdda-3a55714c3f3f · outbound

This paper cites SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation

Reference 52

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation e8ccf111-bdb5-4031-bce0-eb48c547b525 · outbound

This paper cites RPVNet: A Deep and Efficient Range-Point-V oxel Fusion Network for LiDAR Point Cloud Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation RPVNet: A Deep and Efficient Range-Point-V oxel Fusion Network for LiDAR Point Cloud Segmentation

Reference 53

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation dfb41892-effe-46c6-9cd4-0a99497eafd7 · outbound

This paper cites Mul- timodal learning with transformers: A survey.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2023.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Mul- timodal learning with transformers: A survey.IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 2023

Reference 54

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation a87266f2-129a-4873-aec9-32724deb4d91 · outbound

This paper cites 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation 2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds

Reference 55

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 613a33d1-2689-4989-81a1-f334367d9365 · outbound

This paper cites Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Efficient Point Cloud Segmentation with Geometry-Aware Sparse Networks

Reference 56

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 6c5e1588-baf1-4419-84a7-8c02136b3d1c · outbound

This paper cites Litept: Lighter yet stronger point transformer.CVPR, 2026.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Litept: Lighter yet stronger point transformer.CVPR, 2026

Reference 57

Resolution: unresolved; no resolver link, observed 2026-06-28T22:35:40.495590Z

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Observation 2767a850-0d6b-4254-856b-2346a05b6508 · outbound

This paper cites Scaling vision transformers.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Scaling vision transformers

Reference 58

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Observation 24176d89-68c1-4355-9de6-78417d168bca · outbound

This paper cites Deep FusionNet for Point Cloud Semantic Seg- mentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Deep FusionNet for Point Cloud Semantic Seg- mentation

Reference 59

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Observation cbeb37af-3491-4003-a14b-f8a32f24eb16 · outbound

This paper cites PolarNet: An Improved Grid Representation for On- line LiDAR Point Clouds Semantic Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation PolarNet: An Improved Grid Representation for On- line LiDAR Point Clouds Semantic Segmentation

Reference 60

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Observation 764c795d-531c-4c8e-a5ce-0dbaab493f35 · outbound

This paper cites Torr, and Vladlen Koltun.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Torr, and Vladlen Koltun

Reference 61

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Observation efc75ac5-ee81-4a2d-8245-4d8306008814 · outbound

This paper cites SV ASeg: Sparse V oxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation SV ASeg: Sparse V oxel-Based Attention for 3D LiDAR Point Cloud Semantic Segmentation

Reference 62

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Observation c3674a73-f360-4ae1-98db-7accc07f904e · outbound

This paper cites Cylindrical and Asymmetrical 3D Convolution Net- works for LiDAR Segmentation.

Vanilla ViT for Automotive Point Cloud Semantic Segmentation Cylindrical and Asymmetrical 3D Convolution Net- works for LiDAR Segmentation

Reference 63

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