mlsanityData to 7 Oct 2026

Few-shot 3D LiDAR Semantic Segmentation for Autonomous Driving

arXiv 2302.08785 ↗First posted 17 Feb 2023cs.RO cs.CVcited by 3

Not on the map. Why?

Abstract (arXiv)

In autonomous driving, the novel objects and lack of annotations challenge the traditional 3D LiDAR semantic segmentation based on deep learning. Few-shot learning is a feasible way to solve these issues. However, currently few-shot semantic segmentation methods focus on camera data, and most of them only predict the novel classes without considering the base classes. This setting cannot be directly applied to autonomous driving due to safety concerns. Thus, we propose a few-shot 3D LiDAR semantic segmentation method that predicts both novel classes and base classes simultaneously. Our method tries to solve the background ambiguity problem in generalized few-shot semantic segmentation. We first review the original cross-entropy and knowledge distillation losses, then propose a new loss function that incorporates the background information to achieve 3D LiDAR few-shot semantic segmentation. Extensive experiments on SemanticKITTI demonstrate the effectiveness of our method.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 23 of its 36 references that are in the map. First posted on arXiv 17 Feb 2023.

Builds on

23 of 36 references.

References · 23

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  3. 2016Learning without Forgettingcited by 2,465
  4. 2019Similarity-Preserving Knowledge Distillationcited by 507
  5. 2019PANet: Few-Shot Image Semantic Segmentation with Prototype Alignmentcited by 449
  6. 2017One-Shot Learning for Semantic Segmentationcited by 337
  7. 2020SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Drivingcited by 226
  8. 2019CANet: Class-Agnostic Segmentation Networks with Iterative Refinement and Attentive Few-Shot Learningcited by 217
  9. 2020Prototype Mixture Models for Few-shot Semantic Segmentationcited by 190
  10. 2020Modeling the Background for Incremental Learning in Semantic Segmentationcited by 189
  11. 2019Meta R-CNN : Towards General Solver for Instance-level Few-shot Learningcited by 182
  12. 2020Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Reviewcited by 114
11 more
  1. 2021Self-Guided and Cross-Guided Learning for Few-Shot Segmentationcited by 92
  2. 2020Few-shot 3D Point Cloud Semantic Segmentationcited by 77
  3. 2020Generalized Few-shot Semantic Segmentationcited by 59
  4. 2021Pixel-by-Pixel Cross-Domain Alignment for Few-Shot Semantic Segmentationcited by 15
  5. 2020Are We Hungry for 3D LiDAR Data for Semantic Segmentation? A Survey and Experimental Studycited by 12
  6. 2020Compositional Prototype Network with Multi-view Comparision for Few-Shot Point Cloud Semantic Segmentationcited by 12
  7. 2022Self-Regularized Prototypical Network for Few-Shot Semantic Segmentationcited by 12
  8. 2022RangeSeg: Range-Aware Real Time Segmentation of 3D LiDAR Point Cloudscited by 11
  9. 2021Generalized Few-Shot Semantic Segmentation: All You Need is Fine-Tuningcited by 8
  10. 2020Residual-driven Fuzzy C-Means Clustering for Image Segmentationcited by 3
  11. 2020G-image Segmentation: Similarity-preserving Fuzzy C-Means with Spatial Information Constraint in Wavelet Spacecited by 3

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Cited by year

Citing papers per year: 2024: 3.

3 papers.

  1. 2024Delving into Multi-modal Multi-task Foundation Models for Road Scene Understanding: From Learning Paradigm Perspectivescited by 22
  2. 2024Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditionscited by 0
  3. 2024TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentationcited by 0