DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving | Paper Citation Record

Observation e7ccd08f-aff5-42c9-a71d-308baea03215 · outbound

This paper cites 4d- former: Multimodal4dpanopticsegmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving 4d- former: Multimodal4dpanopticsegmentation

Reference 1

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Observation 104618e0-fb02-4666-a630-761f020b105b · outbound

This paper cites Fantrack: 3d multi- object tracking with feature association network.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Fantrack: 3d multi- object tracking with feature association network

Reference 2

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Observation 4681906b-0448-4861-9c19-3fcb07ab9b5d · outbound

This paper cites Semantickitti: Adatasetforsemanticsceneunderstandingoflidarsequences.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Semantickitti: Adatasetforsemanticsceneunderstandingoflidarsequences

Reference 3

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Observation 71b037a6-349a-4251-8dea-ab7f7f59526f · outbound

This paper cites Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Depth Pro: Sharp Monocular Metric Depth in Less Than a Second

Reference 4

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Observation b88df963-0bb5-4e06-b6dd-88cb9b7385bd · outbound

This paper cites Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes.IEEE Robotics and Automation Letters, 2025.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Cafuser: Condition-aware multimodal fusion for robust semantic perception of driving scenes.IEEE Robotics and Automation Letters, 2025

Reference 5

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Observation 5fe7c361-e909-487e-a23f-343f72140029 · outbound

This paper cites Dgfusion: Depth-guidedsensor fusion for robust semantic perception.IEEE Robotics and Automation Letters, 2026.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Dgfusion: Depth-guidedsensor fusion for robust semantic perception.IEEE Robotics and Automation Letters, 2026

Reference 6

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Observation 629f1746-cd28-40d7-b325-81a6fd4a21a3 · outbound

This paper cites End- to-end object detection with transformers.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving End- to-end object detection with transformers

Reference 7

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Observation d3c08c3a-227a-4d94-8377-117a89f3abf8 · outbound

This paper cites Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation

Reference 8

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Observation ed09b1b6-ba50-4dd9-8216-f56560a16bdf · outbound

This paper cites Per- pixel classification is not all you need for semantic segmenta- tion.NeurIPS, 2021.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Per- pixel classification is not all you need for semantic segmenta- tion.NeurIPS, 2021

Reference 9

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Observation fa649f8e-6a73-4c72-b738-e879719cd41b · outbound

This paper cites Masked-attention mask trans- former for universal image segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Masked-attention mask trans- former for universal image segmentation

Reference 10

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Observation f98af75d-6873-4062-836c-1b15b4df02b1 · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving The cityscapes dataset for semantic urban scene understanding

Reference 11

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Observation b3608c8c-175d-462d-82b4-f35ef0a80e3e · outbound

This paper cites Depth map prediction from a single image using a multi-scale deep network.NeurIPS, 2014.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Depth map prediction from a single image using a multi-scale deep network.NeurIPS, 2014

Reference 12

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Observation dd13ff7d-249f-414e-af9d-84530f8b9109 · outbound

This paper cites Cc-3dt: Panoramic 3d object tracking via cross-camera fusion.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Cc-3dt: Panoramic 3d object tracking via cross-camera fusion

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Observation e1ae02ae-b5a1-4ab0-b36f-ebbc8ec46178 · outbound

This paper cites Qdtrack: Quasi-dense similarity learning for appearance-only multiple object tracking.T-PAMI, 2023.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Qdtrack: Quasi-dense similarity learning for appearance-only multiple object tracking.T-PAMI, 2023

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Observation 960e1f45-239a-41d6-837d-0e2c8ea603c3 · outbound

This paper cites Panopticdepth: A unified framework for depth-aware panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Panopticdepth: A unified framework for depth-aware panoptic segmentation

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Observation a8688b36-f9f4-42ee-95ac-5256b4719041 · outbound

This paper cites Are we ready for autonomous driving? the kitti vision benchmark suite.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Are we ready for autonomous driving? the kitti vision benchmark suite

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Observation ee891143-37ae-460c-b955-a994e7d9964f · outbound

This paper cites Deep residual learning for image recognition.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Deep residual learning for image recognition

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Observation e09c0a94-d6d7-4491-abb4-c6d3c7b3336a · outbound

This paper cites Mask r-cnn.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Mask r-cnn

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Observation ac62a910-2d62-4214-8434-af1965080a1a · outbound

This paper cites Monocular quasi-dense 3d object tracking.T-PAMI, 2022.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Monocular quasi-dense 3d object tracking.T-PAMI, 2022

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Observation 9b2b050f-b9f5-413c-a2a6-c0c3d1ac6d16 · outbound

This paper cites Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Metric3Dv2: A Versatile Monocular Geometric Foundation Model for Zero-shot Metric Depth and Surface Normal Estimation

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Observation 908a1ca5-c0cb-4a33-ba70-b24d52d36ea3 · outbound

This paper cites Computervisionforautonomousvehicles: Problems,datasets and state of the art.Foundations and Trends in Computer Graphics and Vision, 2020.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Computervisionforautonomousvehicles: Problems,datasets and state of the art.Foundations and Trends in Computer Graphics and Vision, 2020

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Observation e9c382c9-3b29-4fd9-957c-c8f22434f144 · outbound

This paper cites Uni-dvps: Unified model for depth-aware video panoptic segmentation.IEEE Robotics and Automation Letters (RA-L), 2024.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Uni-dvps: Unified model for depth-aware video panoptic segmentation.IEEE Robotics and Automation Letters (RA-L), 2024

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Observation 706ed449-0b74-440f-bedc-c7ff67cfaa87 · outbound

This paper cites Eager- mot: 3d multi-object tracking via sensor fusion.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Eager- mot: 3d multi-object tracking via sensor fusion

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Observation 6a4c67a3-c4d8-4f08-8bc9-18acce1cdb1e · outbound

This paper cites Video panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Video panoptic segmentation

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Observation 8431cba7-3505-4bdb-a304-172903e9ff65 · outbound

This paper cites Semantic hierarchy- guided adversarial attack for autonomous driving.IEEE Robotics and Automation Letters, 2025.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Semantic hierarchy- guided adversarial attack for autonomous driving.IEEE Robotics and Automation Letters, 2025

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Observation 8341df67-c676-41e0-a713-ee6993ec9b67 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Adam: A Method for Stochastic Optimization

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Observation f48d9ef6-f1ea-47ae-a219-83e789ac39cb · outbound

This paper cites Panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Panoptic segmentation

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Observation e568084a-fe54-43f9-9f0f-94df7f8424d1 · outbound

This paper cites Tracking every thing in the wild.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Tracking every thing in the wild

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Observation 4f88d586-dfdb-43c8-a296-fa7bb61e904e · outbound

This paper cites Fully convolutional networks for panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Fully convolutional networks for panoptic segmentation

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Observation 9a44bc0b-ebf6-45ca-9821-1e8abe654c5c · outbound

This paper cites Object-centric learning with slot attention.NeurIPS, 2020.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Object-centric learning with slot attention.NeurIPS, 2020

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Observation ca56e4c1-4b4e-43a2-a8a3-495f03dbff13 · outbound

This paper cites V-net: Fully convolutional neural networks for volumetric medical image segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving V-net: Fully convolutional neural networks for volumetric medical image segmentation

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Observation feee1c56-af29-4f56-9ff4-d0d72290ba51 · outbound

This paper cites Scalableparallelprogrammingwithcuda: Iscudatheparallel programming model that application developers have been waiting for?Queue, 2008.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Scalableparallelprogrammingwithcuda: Iscudatheparallel programming model that application developers have been waiting for?Queue, 2008

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Observation fda85996-07a7-4c75-a718-5b0e6a2891a3 · outbound

This paper cites Quasi-densesimilaritylearning for multiple object tracking.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Quasi-densesimilaritylearning for multiple object tracking

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Observation d6833738-aafc-4a96-9d09-277767362d76 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Pytorch: An imperative style, high-performance deep learning library

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Observation 1503eff7-081c-4f87-97a2-9e3ad3468d18 · outbound

This paper cites Monodvps: A self- supervised monocular depth estimation approach to depth- aware video panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Monodvps: A self- supervised monocular depth estimation approach to depth- aware video panoptic segmentation

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Observation f7cfccde-ae7d-4baa-8e71-7ba4a56028a7 · outbound

This paper cites iDisc: Internal discretization for monocular depth estimation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving iDisc: Internal discretization for monocular depth estimation

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Observation 62dd0c55-fbfc-44ff-a448-6c31f5cfd08c · outbound

This paper cites Unidepth: Universalmonocularmetricdepthestimation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Unidepth: Universalmonocularmetricdepthestimation

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Observation 8c48bafa-9ab5-46c8-8313-1e0d22ec9e4f · outbound

This paper cites UniK3D: Universal camera monocular 3d estimation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving UniK3D: Universal camera monocular 3d estimation

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Observation 51b98ed6-641b-4035-800e-6ec707d18ad2 · outbound

This paper cites UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler

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Observation 7b484f29-e729-4d61-928a-6ef8792299da · outbound

This paper cites Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation

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This paper cites Balancing shared and task-specific repre- sentations: A hybrid approach to depth-aware video panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Balancing shared and task-specific repre- sentations: A hybrid approach to depth-aware video panoptic segmentation

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Observation 8d3b4382-3177-48ea-a81c-b23bf2d8ebf0 · outbound

This paper cites Attention is all you need.NeurIPS, 2017.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Attention is all you need.NeurIPS, 2017

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This paper cites A good foundation is worth many labels: Label-efficient panoptic segmentation.IEEE Robotics and Automation Letters, 2024.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving A good foundation is worth many labels: Label-efficient panoptic segmentation.IEEE Robotics and Automation Letters, 2024

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Observation f19527a7-311e-4bcb-a0d1-55161b116b22 · outbound

This paper cites Fastdepth: Fast monocular depth estima- tion on embedded systems.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Fastdepth: Fast monocular depth estima- tion on embedded systems

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Observation 6aac35ad-b1e8-41e2-9f6c-cf3e0ea24ad3 · outbound

This paper cites In defense of online models for video instance segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving In defense of online models for video instance segmentation

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DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Efficientdps: Efficient and end-to-end depth- aware panoptic segmentation

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Observation 9f0c65ef-85af-4c6b-b49c-27882d5a1ff7 · outbound

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DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Upsnet: A unified panoptic segmentation network

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Observation d7ce6714-b229-42fa-828b-bdd56d5bb18d · outbound

This paper cites Video instance segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Video instance segmentation

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Observation b852eabf-9494-4d1d-819d-41e6d6a226a4 · outbound

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DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving k-means mask transformer

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Observation ef991ac2-7bc2-4b88-ba55-81b24f328dd9 · outbound

This paper cites Polyphonicformer: unified query learning for depth-aware video panoptic segmentation.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Polyphonicformer: unified query learning for depth-aware video panoptic segmentation

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

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving K-net: Towards unified image segmentation.NeurIPS,

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Observation e14b9012-c27e-49f6-9fe8-279d7fb6d50a · outbound

This paper cites Does computer vision matter for action?Science Robotics, 2019.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Does computer vision matter for action?Science Robotics, 2019

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Observation 18b9b8da-f1dd-4fe5-a2aa-e1ac836b66a5 · outbound

This paper cites Deformable DETR: Deformable Transformers for End-to-End Object Detection.

DVPSFormer: Efficient Online Depth-aware Video Panoptic Segmentation for Autonomous Driving Deformable DETR: Deformable Transformers for End-to-End Object Detection

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