mlsanityData to 7 Oct 2026

Identifying Unknown Instances for Autonomous Driving

arXiv 1910.11296 ↗First posted 24 Oct 2019cs.CV cs.LG cs.ROcited by 73

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Abstract (arXiv)

In the past few years, we have seen great progress in perception algorithms, particular through the use of deep learning. However, most existing approaches focus on a few categories of interest, which represent only a small fraction of the potential categories that robots need to handle in the real-world. Thus, identifying objects from unknown classes remains a challenging yet crucial task. In this paper, we develop a novel open-set instance segmentation algorithm for point clouds which can segment objects from both known and unknown classes in a holistic way. Our method uses a deep convolutional neural network to project points into a category-agnostic embedding space in which they can be clustered into instances irrespective of their semantics. Experiments on two large-scale self-driving datasets validate the effectiveness of our proposed method.

Key facts. Cited by 73 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Oct 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 12; 2023: 16; 2024: 14; 2025: 7; 2026: 5.

73 papers.

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  1. 2021A Survey of Uncertainty in Deep Neural Networkscited by 675
  2. 2021Multimodal Virtual Point 3D Detectioncited by 166
  3. 2023Open-Vocabulary Point-Cloud Object Detection without 3D Annotationcited by 81
  4. 20214D Panoptic LiDAR Segmentationcited by 64
  5. 2022Motion Inspired Unsupervised Perception and Prediction in Autonomous Drivingcited by 59
  6. 2021Object DGCNN: 3D Object Detection using Dynamic Graphscited by 58
  7. 2021Semantics for Robotic Mapping, Perception and Interaction: A Surveycited by 56
  8. 2021Opening up Open-World Trackingcited by 50
  9. 2024Better Call SAL: Towards Learning to Segment Anything in Lidarcited by 47
  10. 2021Self-Supervised Pillar Motion Learning for Autonomous Drivingcited by 44
  11. 2022Towards Long-Tailed 3D Detectioncited by 40
  12. 2023Unsupervised 3D Perception with 2D Vision-Language Distillation for Autonomous Drivingcited by 37
38 more
  1. 2021An Application-Driven Conceptualization of Corner Cases for Perception in Highly Automated Drivingcited by 33
  2. 2023TBP-Former: Learning Temporal Bird's-Eye-View Pyramid for Joint Perception and Prediction in Vision-Centric Autonomous Drivingcited by 30
  3. 2021ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Buildingcited by 29
  4. 2021Open-set 3D Object Detectioncited by 29
  5. 2021Patchwork: Concentric Zone-based Region-wise Ground Segmentation with Ground Likelihood Estimation Using a 3D LiDAR Sensorcited by 28
  6. 2023Towards Unsupervised Object Detection From LiDAR Point Cloudscited by 22
  7. 2023OpenSight: A Simple Open-Vocabulary Framework for LiDAR-Based Object Detectioncited by 22
  8. 2021Video Class Agnostic Segmentation Benchmark for Autonomous Drivingcited by 21
  9. 2022Out-of-Distribution Detection for LiDAR-based 3D Object Detectioncited by 17
  10. 2023Perception Datasets for Anomaly Detection in Autonomous Driving: A Surveycited by 16
  11. 2023UGainS: Uncertainty Guided Anomaly Instance Segmentationcited by 15
  12. 2024SeMoLi: What Moves Together Belongs Togethercited by 13
  13. 2021Trust Your Robots! Predictive Uncertainty Estimation of Neural Networks with Sparse Gaussian Processescited by 12
  14. 2022NeuralBF: Neural Bilateral Filtering for Top-down Instance Segmentation on Point Cloudscited by 12
  15. 2022LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Networkcited by 11
  16. 20223DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point Cloudscited by 11
  17. 2022PointCaM: Cut-and-Mix for Open-Set Point Cloud Learningcited by 11
  18. 2023Long-Tailed 3D Detection via Multi-Modal Fusioncited by 10
  19. 2025Spotting the Unexpected (STU): A 3D LiDAR Dataset for Anomaly Segmentation in Autonomous Drivingcited by 10
  20. 2021Learning to Better Segment Objects from Unseen Classes with Unlabeled Videoscited by 9
  21. 2022Segmenting Known Objects and Unseen Unknowns without Prior Knowledgecited by 9
  22. 2024Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detectioncited by 9
  23. 2024OoDIS: Anomaly Instance Segmentation and Detection Benchmarkcited by 8
  24. 2021RaidaR: A Rich Annotated Image Dataset of Rainy Street Scenescited by 7
  25. 2024Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scenecited by 7
  26. 2024Lidar Panoptic Segmentation in an Open Worldcited by 7
  27. 2025Prior2Former -- Evidential Modeling of Mask Transformers for Assumption-Free Open-World Panoptic Segmentationcited by 7
  28. 2021Uncertainty Aware Proposal Segmentation for Unknown Object Detectioncited by 6
  29. 2024Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signalscited by 5
  30. 2025Open-Set LiDAR Panoptic Segmentation Guided by Uncertainty-Aware Learningcited by 5
  31. 2022Multimodal Detection of Unknown Objects on Roads for Autonomous Drivingcited by 4
  32. 2022In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehiclescited by 3
  33. 2023ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Datacited by 3
  34. 2023Efficient Transformer-based 3D Object Detection with Dynamic Token Haltingcited by 3
  35. 2021Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognitioncited by 2
  36. 2022Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognitioncited by 2
  37. 2023Latent Space Energy-based Model for Fine-grained Open Set Recognitioncited by 2
  38. 2023A Metacognitive Approach to Out-of-Distribution Detection for Segmentationcited by 2

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