mlsanityData to 14 Oct 2026

One for All: Multi-Domain Joint Training for Point Cloud Based 3D Object Detection

arXiv 2411.01584 ↗First posted 3 Nov 2024cs.CVcited by 8

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Abstract (arXiv)

The current trend in computer vision is to utilize one universal model to address all various tasks. Achieving such a universal model inevitably requires incorporating multi-domain data for joint training to learn across multiple problem scenarios. In point cloud based 3D object detection, however, such multi-domain joint training is highly challenging, because large domain gaps among point clouds from different datasets lead to the severe domain-interference problem. In this paper, we propose OneDet3D, a universal one-for-all model that addresses 3D detection across different domains, including diverse indoor and outdoor scenes, within the same framework and only one set of parameters. We propose the domain-aware partitioning in scatter and context, guided by a routing mechanism, to address the data interference issue, and further incorporate the text modality for a language-guided classification to unify the multi-dataset label spaces and mitigate the category interference issue. The fully sparse structure and anchor-free head further accommodate point clouds with significant scale disparities. Extensive experiments demonstrate the strong universal ability of OneDet3D to utilize only one trained model for addressing almost all 3D object detection tasks.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 48 of its 56 references that are in the map. First posted on arXiv 3 Nov 2024.

Builds on

48 of 56 references.

References · 48

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36 more
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  4. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 891
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  11. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 222
  12. 2021Group-Free 3D Object Detection via Transformerscited by 215
  13. 2022Unifying Voxel-based Representation with Transformer for 3D Object Detectioncited by 202
  14. 2022PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learningcited by 187
  15. 2022Focal Sparse Convolutional Networks for 3D Object Detectioncited by 142
  16. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  17. 2021Simple multi-dataset detectioncited by 127
  18. 2020H3DNet: 3D Object Detection Using Hybrid Geometric Primitivescited by 123
  19. 2021Improving 3D Object Detection with Channel-wise Transformercited by 108
  20. 2021FCAF3D: Fully Convolutional Anchor-Free 3D Object Detectioncited by 97
  21. 2019Towards Universal Object Detection by Domain Attentioncited by 89
  22. 2023Towards Large-scale 3D Representation Learning with Multi-dataset Point Prompt Trainingcited by 86
  23. 2023Open-Vocabulary Point-Cloud Object Detection without 3D Annotationcited by 81
  24. 2020Generative Sparse Detection Networks for 3D Single-shot Object Detectioncited by 69
  25. 2020Object Detection with a Unified Label Space from Multiple Datasetscited by 65
  26. 2023Detecting Everything in the Open World: Towards Universal Object Detectioncited by 62
  27. 2022CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Cloudscited by 58
  28. 2023CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detectioncited by 52
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2025Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyondcited by 18
  2. 2026Few-Shot Incremental 3D Object Detection in Dynamic Indoor Environmentscited by 2
  3. 2025GLRD: Global-Local Collaborative Reason and Debate with PSL for 3D Open-Vocabulary Detectioncited by 1
  4. 2025YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videoscited by 1
  5. 2025Domain Adaptation for Different Sensor Configurations in 3D Object Detectioncited by 0
  6. 2025DoReMi: Bridging 3D Domains via Topology-Aware Domain-Representation Mixture of Expertscited by 0
  7. 2026Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detectioncited by 0
  8. 2026STAR: A Spatial-Topology Aware Routing Framework for Generalizable 3D Scene Understandingcited by 0