mlsanityData to 14 Oct 2026

Intrinsic-feature-guided 3D Object Detection

arXiv 2504.00382 ↗First posted 1 Apr 2025cs.CVcited by 0

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Abstract (arXiv)

LiDAR-based 3D object detection is essential for autonomous driving systems. However, LiDAR point clouds may appear to have sparsity, uneven distribution, and incomplete structures, significantly limiting the detection performance. In road driving environments, target objects referring to vehicles, pedestrians and cyclists are well-suited for enhancing representation through the complete template guidance, considering their grid and topological structures. Therefore, this paper presents an intrinsic-feature-guided 3D object detection method based on a template-assisted feature enhancement module, which extracts intrinsic features from relatively generalized templates and provides rich structural information for foreground objects. Furthermore, a proposal-level contrastive learning mechanism is designed to enhance the feature differences between foreground and background objects. The proposed modules can act as plug-and-play components and improve the performance of multiple existing methods. Extensive experiments illustrate that the proposed method achieves the highly competitive detection results. Code will be available at https://github.com/zhangwanjingjj/IfgNet.git.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 46 references that are in the map. First posted on arXiv 1 Apr 2025.

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37 of 46 references.

References · 37

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25 more
  1. 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
  2. 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 410
  3. 2018PCN: Point Completion Networkcited by 384
  4. 2019From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Networkcited by 368
  5. 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
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  8. 2021DetCo: Unsupervised Contrastive Learning for Object Detectioncited by 205
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  13. 2022Fully Sparse 3D Object Detectioncited by 84
  14. 2021Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detectioncited by 79
  15. 2020SSN: Shape Signature Networks for Multi-class Object Detection from Point Cloudscited by 74
  16. 2022Point Density-Aware Voxels for LiDAR 3D Object Detectioncited by 70
  17. 2021Behind the Curtain: Learning Occluded Shapes for 3D Object Detectioncited by 58
  18. 2022SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detectioncited by 52
  19. 2022ProposalContrast: Unsupervised Pre-training for LiDAR-based 3D Object Detectioncited by 43
  20. 2020RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representationcited by 37
  21. 2020Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detectioncited by 31
  22. 2023PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformercited by 21
  23. 2023PG-RCNN: Semantic Surface Point Generation for 3D Object Detectioncited by 12
  24. 2022CG-SSD: Corner Guided Single Stage 3D Object Detection from LiDAR Point Cloudcited by 5
  25. 2023Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMScited by 3

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