Bounding Box Disparity: 3D Metrics for Object Detection With Full Degree of Freedom
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Abstract (arXiv)
The most popular evaluation metric for object detection in 2D images is Intersection over Union (IoU). Existing implementations of the IoU metric for 3D object detection usually neglect one or more degrees of freedom. In this paper, we first derive the analytic solution for three dimensional bounding boxes. As a second contribution, a closed-form solution of the volume-to-volume distance is derived. Finally, the Bounding Box Disparity is proposed as a combined positive continuous metric. We provide open source implementations of the three metrics as standalone python functions, as well as extensions to the Open3D library and as ROS nodes.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 18 references that are in the map. First posted on arXiv 8 Jul 2022.
Builds on
11 of 18 references.
References · 11
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,117
- 2018Open3D: A Modern Library for 3D Data Processingcited by 721
- 2021Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AIcited by 592
- 2021ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Datacited by 454
- 2017Joint 2D-3D-Semantic Data for Indoor Scene Understandingcited by 406
- 2019Learning joint reconstruction of hands and manipulated objectscited by 378
- 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
- 20163D Bounding Box Estimation Using Deep Learning and Geometrycited by 278
- 2020Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotationscited by 155
- 2021FCAF3D: Fully Convolutional Anchor-Free 3D Object Detectioncited by 97
- 2020A Review on Object Pose Recovery: from 3D Bounding Box Detectors to Full 6D Pose Estimatorscited by 18
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.