mlsanityData to 14 Oct 2026

Panoptic Perception for Autonomous Driving: A Survey

arXiv 2408.15388 ↗First posted 27 Aug 2024cs.RO cs.CV cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

Panoptic perception represents a forefront advancement in autonomous driving technology, unifying multiple perception tasks into a singular, cohesive framework to facilitate a thorough understanding of the vehicle's surroundings. This survey reviews typical panoptic perception models for their unique inputs and architectures and compares them to performance, responsiveness, and resource utilization. It also delves into the prevailing challenges faced in panoptic perception and explores potential trajectories for future research. Our goal is to furnish researchers in autonomous driving with a detailed synopsis of panoptic perception, positioning this survey as a pivotal reference in the ever-evolving landscape of autonomous driving technologies.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 80 of its 113 references that are in the map. First posted on arXiv 27 Aug 2024.

Builds on

80 of 113 references.

References · 80

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  4. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  5. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  6. 2020End-to-End Object Detection with Transformerscited by 7,414
  7. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  8. 2016Feature Pyramid Networks for Object Detectioncited by 5,036
  9. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
  10. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,523
  11. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,444
  12. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
38 more
  1. 2018Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentationcited by 3,223
  2. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  3. 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,940
  4. 2016Aggregated Residual Transformations for Deep Neural Networkscited by 2,546
  5. 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,451
  6. 2015Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codingcited by 2,329
  7. 2013Rich feature hierarchies for accurate object detection and semantic segmentationcited by 2,162
  8. 2019Searching for MobileNetV3cited by 1,994
  9. 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,715
  10. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,630
  11. 2020Knowledge Distillation: A Surveycited by 1,603
  12. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  13. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
  14. 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
  15. 2018BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learningcited by 1,288
  16. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,218
  17. 2020Center-based 3D Object Detection and Trackingcited by 1,144
  18. 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,136
  19. 2020Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3Dcited by 1,044
  20. 2019EfficientDet: Scalable and Efficient Object Detectioncited by 1,020
  21. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  22. 2022BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representationcited by 850
  23. 2017GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networkscited by 834
  24. 2014Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognitioncited by 720
  25. 2019CenterNet: Keypoint Triplets for Object Detectioncited by 654
  26. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 620
  27. 2020Sparse R-CNN: End-to-End Object Detection with Learnable Proposalscited by 495
  28. 2022PETRv2: A Unified Framework for 3D Perception from Multi-Camera Imagescited by 352
  29. 2019GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyondcited by 331
  30. 2020Exploring Self-attention for Image Recognitioncited by 313
  31. 2022BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervisioncited by 302
  32. 2016ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentationcited by 291
  33. 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
  34. 2020One Thousand and One Hours: Self-driving Motion Prediction Datasetcited by 240
  35. 2017Tversky loss function for image segmentation using 3D fully convolutional deep networkscited by 228
  36. 2017Submanifold Sparse Convolutional Networkscited by 223
  37. 2018Orthographic Feature Transform for Monocular 3D Object Detectioncited by 202
  38. 2019CenterMask : Real-Time Anchor-Free Instance Segmentationcited by 201

50 most cited of 80 All 80 (JSON)

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Benchmarking Image Perturbations for Testing Automated Driving Assistance Systemscited by 4
  2. 2025FIN: Fast Inference Network for Map Segmentationcited by 0
  3. 2026From Review to Design: Ethical Multimodal Driver Monitoring Systems for Risk Mitigation, Incident Response, and Accountability in Automated Vehiclescited by 0
  4. 2026Real-World Perturbation Testing of Autonomous Driving Systemscited by 0