Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing | Paper Citation Record

Observation 41472a22-b086-4ea7-8896-ea43477dcc58 · outbound

This paper cites Segment Anything,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Segment Anything,

Reference 1

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Observation 3f82574d-1afd-4eec-81f4-ae4613d7b267 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing DINOv2: Learning Robust Visual Features without Supervision

Reference 2

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This paper cites Depth Anything: Unleashing the power of large-scale unlabeled data,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Depth Anything: Unleashing the power of large-scale unlabeled data,

Reference 3

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Observation ca7be2aa-d027-4b63-bcc4-cbda744d06b5 · outbound

This paper cites ViPOcc: leveraging visual priors from vision foundation models for single-view 3d occupancy prediction,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing ViPOcc: leveraging visual priors from vision foundation models for single-view 3d occupancy prediction,

Reference 4

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This paper cites Few-shot object detection with foundation models,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Few-shot object detection with foundation models,

Reference 5

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Observation cb7b4d2e-d352-43dd-b883-f832a4d4ff8d · outbound

This paper cites Playing to vision foundation model’s strengths in stereo matching,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Playing to vision foundation model’s strengths in stereo matching,

Reference 6

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This paper cites Joint depth prediction and semantic segmentation with multi-view SAM,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Joint depth prediction and semantic segmentation with multi-view SAM,

Reference 7

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Observation 74a75f9d-e5cc-4362-a255-6301f75a3107 · outbound

This paper cites Bridging the domain gap: Self-supervised 3D scene understanding with foundation models,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Bridging the domain gap: Self-supervised 3D scene understanding with foundation models,

Reference 8

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This paper cites HAPNet: Toward superior RGB-Thermal scene parsing via hybrid, asymmetric, and progressive heterogeneous feature fusion,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing HAPNet: Toward superior RGB-Thermal scene parsing via hybrid, asymmetric, and progressive heterogeneous feature fusion,

Reference 9

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This paper cites SNE-RoadSeg: Incorporating surface normal informa- tion into semantic segmentation for accurate freespace detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing SNE-RoadSeg: Incorporating surface normal informa- tion into semantic segmentation for accurate freespace detection,

Reference 10

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Observation aab1c3bf-9713-4b57-a360-1ddd9f7e1830 · outbound

This paper cites ORFD: A dataset and benchmark for OFF-Road freespace detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing ORFD: A dataset and benchmark for OFF-Road freespace detection,

Reference 11

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This paper cites MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing MFNet: Towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes,

Reference 12

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This paper cites FuseNet: Incorporating depth into semantic segmen- tation via fusion-based cnn architecture,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing FuseNet: Incorporating depth into semantic segmen- tation via fusion-based cnn architecture,

Reference 13

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This paper cites Pothole detection based on disparity transformation and road surface modeling,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Pothole detection based on disparity transformation and road surface modeling,

Reference 14

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This paper cites Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Graph attention layer evolves semantic segmentation for road pothole detection: A benchmark and algorithms,

Reference 15

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Observation eb8a03d2-c152-4b05-8ee2-e28ae265f70f · outbound

This paper cites These maps are made by propagation: Adapting deep stereo networks to road scenarios with decisive disparity diffusion,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing These maps are made by propagation: Adapting deep stereo networks to road scenarios with decisive disparity diffusion,

Reference 16

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This paper cites S 3M-Net: Joint learning of semantic segmentation and stereo matching for autonomous driving,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing S 3M-Net: Joint learning of semantic segmentation and stereo matching for autonomous driving,

Reference 17

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Observation 8cd1df51-977a-43dd-ae37-70b9c5c6981a · outbound

This paper cites Online, target-free LiDAR-camera extrinsic calibration via cross-modal mask matching,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Online, target-free LiDAR-camera extrinsic calibration via cross-modal mask matching,

Reference 18

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Observation 65420ead-46fe-48d2-863f-ec9a21ba3c07 · outbound

This paper cites Depth Anything V2.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Depth Anything V2

Reference 19

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Observation c79956bc-c4dc-4ee8-b51a-4dcf2fb59d65 · outbound

This paper cites Three ways to improve semantic segmentation with self-supervised depth estimation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Three ways to improve semantic segmentation with self-supervised depth estimation,

Reference 20

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This paper cites Learning to relate depth and semantics for unsupervised domain adaptation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Learning to relate depth and semantics for unsupervised domain adaptation,

Reference 21

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This paper cites Efficient RGB-D semantic segmentation for indoor scene analysis,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Efficient RGB-D semantic segmentation for indoor scene analysis,

Reference 22

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This paper cites Learning common and specific features for RGB-D semantic segmentation with deconvolutional networks,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Learning common and specific features for RGB-D semantic segmentation with deconvolutional networks,

Reference 23

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This paper cites Vision Transformer adapter for dense predictions,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Vision Transformer adapter for dense predictions,

Reference 24

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This paper cites Conv-Adapter: Exploring parameter efficient transfer learning for convnets,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Conv-Adapter: Exploring parameter efficient transfer learning for convnets,

Reference 25

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This paper cites CANet: Co-attention network for RGB-D semantic segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing CANet: Co-attention network for RGB-D semantic segmentation,

Reference 26

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This paper cites FANet: Feature aggregation network for RGBD saliency detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing FANet: Feature aggregation network for RGBD saliency detection,

Reference 27

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This paper cites RoadFormer: Duplex Transformer for RGB-Normal se- mantic road scene parsing,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing RoadFormer: Duplex Transformer for RGB-Normal se- mantic road scene parsing,

Reference 28

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This paper cites Roadformer+: Delivering RGB-X scene parsing through scale-aware information decoupling and advanced heterogeneous fea- ture fusion,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Roadformer+: Delivering RGB-X scene parsing through scale-aware information decoupling and advanced heterogeneous fea- ture fusion,

Reference 29

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This paper cites ViT-CoMer: Vision Transformer with convolutional multi- scale feature interaction for dense predictions,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing ViT-CoMer: Vision Transformer with convolutional multi- scale feature interaction for dense predictions,

Reference 30

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This paper cites Robust RGB-D fusion for saliency detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Robust RGB-D fusion for saliency detection,

Reference 31

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This paper cites Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Deep RGB-D saliency detection with depth-sensitive attention and automatic multi-modal fusion,

Reference 32

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This paper cites Gated fully fusion for semantic segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Gated fully fusion for semantic segmentation,

Reference 33

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This paper cites UNet++: Redesigning skip connections to exploit mul- tiscale features in image segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing UNet++: Redesigning skip connections to exploit mul- tiscale features in image segmentation,

Reference 34

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This paper cites The CityScapes dataset for semantic urban scene understanding,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing The CityScapes dataset for semantic urban scene understanding,

Reference 35

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This paper cites Object scene flow for autonomous vehicles,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Object scene flow for autonomous vehicles,

Reference 36

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This paper cites Deep multimodal fusion for semantic image segmen- tation: A survey,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Deep multimodal fusion for semantic image segmen- tation: A survey,

Reference 37

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This paper cites AdapNet: Adaptive semantic segmentation in adverse environmental conditions,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing AdapNet: Adaptive semantic segmentation in adverse environmental conditions,

Reference 38

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This paper cites Locality-sensitive deconvolution networks with gated fusion for RGB-D indoor semantic segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Locality-sensitive deconvolution networks with gated fusion for RGB-D indoor semantic segmentation,

Reference 39

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This paper cites Learning feature fusion in deep learning-based object detector,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Learning feature fusion in deep learning-based object detector,

Reference 40

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This paper cites ECFFNet: Effective and consistent feature fusion network for RGB-T salient object detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing ECFFNet: Effective and consistent feature fusion network for RGB-T salient object detection,

Reference 41

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This paper cites Salient object detection: A discriminative regional fea- ture integration approach,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Salient object detection: A discriminative regional fea- ture integration approach,

Reference 42

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Observation c5c6b398-7c2d-4748-9358-e043bd16fa54 · outbound

This paper cites ExFuse: Enhancing feature fusion for semantic seg- mentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing ExFuse: Enhancing feature fusion for semantic seg- mentation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.506533Z

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This paper cites Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Bi-directional cross-modality feature propagation with separation-and-aggregation gate for RGB-D semantic segmentation,

Reference 44

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This paper cites F 3Net: fusion, feedback and focus for salient object de- tection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing F 3Net: fusion, feedback and focus for salient object de- tection,

Reference 45

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Observation 079cd268-90e8-48e9-826b-55dfc982d192 · outbound

This paper cites Incorporating convolution designs into visual Trans- formers,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Incorporating convolution designs into visual Trans- formers,

Reference 46

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Observation aed10c1f-44c6-4eb2-8858-47144e9d5214 · outbound

This paper cites EfficientNet: Rethinking model scaling for con- volutional neural networks,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing EfficientNet: Rethinking model scaling for con- volutional neural networks,

Reference 47

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Observation 4e0189f4-33e3-4e30-b428-c8f3ad491b92 · outbound

This paper cites Deep residual learning for image recognition,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Deep residual learning for image recognition,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.462329Z

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This paper cites Real-time fusion network for RGB-D semantic seg- mentation incorporating unexpected obstacle detection for road-driving images,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Real-time fusion network for RGB-D semantic seg- mentation incorporating unexpected obstacle detection for road-driving images,

Reference 49

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Observation 5e7a3403-0106-478d-8d6b-7b702b6c751a · outbound

This paper cites THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing THCANet: Two-layer hop cascaded asymptotic network for robot-driving road-scene semantic segmentation in RGB-D images,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.444748Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

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This paper cites Hierarchical dynamic filtering network for RGB-D salient object detection,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Hierarchical dynamic filtering network for RGB-D salient object detection,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.434721Z

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Observation b20c7e2b-7652-442f-9f53-6a6d261d3ead · outbound

This paper cites Layer Normalization.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Layer Normalization

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-08T16:25:11.641036Z

Source-reported events for the cited work

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Observation 479ca3c8-3975-4913-a7ba-6477ee6af807 · outbound

This paper cites Masked-attention mask Transformer for universal image segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Masked-attention mask Transformer for universal image segmentation,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.425344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 7b313d33-c069-4134-9c79-f733d8204ca1 · outbound

This paper cites FaPN: Feature-aligned pyramid network for dense image prediction,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing FaPN: Feature-aligned pyramid network for dense image prediction,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.416855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0e50d385-22d4-44df-9fc3-af21ebf35232 · outbound

This paper cites Integrating low-level and semantic features for object consistent segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Integrating low-level and semantic features for object consistent segmentation,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.408688Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 222ace97-5038-44dc-8641-6e1b419ed1ad · outbound

This paper cites Swin Transformer: hierarchical vision Transformer using shifted windows,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Swin Transformer: hierarchical vision Transformer using shifted windows,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.399323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation fdba2a60-af07-405b-a178-38bf32d17265 · outbound

This paper cites SegFormer: Simple and efficient design for semantic segmentation with Transformers,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing SegFormer: Simple and efficient design for semantic segmentation with Transformers,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.389892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation cf89a2f1-1fea-474f-aa76-79fee7f70df9 · outbound

This paper cites Object-contextual representations for semantic segmen- tation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Object-contextual representations for semantic segmen- tation,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.381433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 71d41a84-0cb1-429c-87fa-942315d7aeac · outbound

This paper cites K-Net: Towards unified image segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing K-Net: Towards unified image segmentation,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.370225Z

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Observation dc9e56c8-7376-40b9-93fa-82241d5ed63c · outbound

This paper cites Expectation-maximization attention networks for semantic segmentation,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing Expectation-maximization attention networks for semantic segmentation,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.361250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 72a014a4-bdca-493f-a341-6517ccf1d32a · outbound

This paper cites RAFT-Stereo: Multilevel recurrent field transforms for stereo matching,.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing RAFT-Stereo: Multilevel recurrent field transforms for stereo matching,

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T16:25:12.353040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f873526a-e012-4150-8e36-5dbeb3a814ea · outbound

This paper cites LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing LiDAR-LLM: Exploring the Potential of Large Language Models for 3D LiDAR Understanding

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-08T16:25:11.676998Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d0502a5-f37c-493f-9e56-04f9354ee14c · outbound

This paper cites DriveLM: Driving with Graph Visual Question Answering.

Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for Generalizable RGB-Depth Driving Scene Parsing DriveLM: Driving with Graph Visual Question Answering

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T16:25:11.684696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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