mlsanityData to 7 Oct 2026

DCDet: Dynamic Cross-based 3D Object Detector

arXiv 2401.07240 ↗First posted 14 Jan 2024cs.CVcited by 2

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Abstract (arXiv)

Recently, significant progress has been made in the research of 3D object detection. However, most prior studies have focused on the utilization of center-based or anchor-based label assignment schemes. Alternative label assignment strategies remain unexplored in 3D object detection. We find that the center-based label assignment often fails to generate sufficient positive samples for training, while the anchor-based label assignment tends to encounter an imbalanced issue when handling objects of varying scales. To solve these issues, we introduce a dynamic cross label assignment (DCLA) scheme, which dynamically assigns positive samples for each object from a cross-shaped region, thus providing sufficient and balanced positive samples for training. Furthermore, to address the challenge of accurately regressing objects with varying scales, we put forth a rotation-weighted Intersection over Union (RWIoU) metric to replace the widely used L1 metric in regression loss. Extensive experiments demonstrate the generality and effectiveness of our DCLA and RWIoU-based regression loss. The Code will be available at https://github.com/Say2L/DCDet.git.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 33 of its 39 references that are in the map. First posted on arXiv 14 Jan 2024.

Builds on

33 of 39 references.

References · 33

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21 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 409
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  12. 2020AutoAssign: Differentiable Label Assignment for Dense Object Detectioncited by 100
  13. 2022PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detectioncited by 99
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  15. 2021AFDetV2: Rethinking the Necessity of the Second Stage for Object Detection from Point Cloudscited by 88
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Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2025FSHNet: Fully Sparse Hybrid Network for 3D Object Detectioncited by 9
  2. 2024FFAM: Feature Factorization Activation Map for Explanation of 3D Detectorscited by 2