Accurate 3D Object Detection using Energy-Based Models
@article{Gustafsson2020Accurate3O, title={Accurate 3D Object Detection using Energy-Based Models}, author={Fredrik K. Gustafsson and Martin Danelljan and T. Schon}, journal={ArXiv}, year={2020}, volume={abs/2012.04634} }
Accurate 3D object detection (3DOD) is crucial for safe navigation of complex environments by autonomous robots. Regressing accurate 3D bounding boxes in cluttered environments based on sparse LiDAR data is however a highly challenging problem. We address this task by exploring recent advances in conditional energy-based models (EBMs) for probabilistic regression. While methods employing EBMs for regression have demonstrated impressive performance on 2D object detection in images, these… CONTINUE READING
Figures and Tables from this paper
References
SHOWING 1-10 OF 67 REFERENCES
LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving
- Computer Science
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2019
- 95
- PDF
Joint 3D Proposal Generation and Object Detection from View Aggregation
- Computer Science
- 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- 2018
- 476
- PDF
CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection
- Computer Science
- 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- 2020
- 3
- PDF
MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships
- Computer Science
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2020
- 8
- PDF
Distance-Normalized Unified Representation for Monocular 3D Object Detection
- Computer Science
- ECCV
- 2020
- 2
- PDF
Joint 3D Instance Segmentation and Object Detection for Autonomous Driving
- Computer Science
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- 2020
- 3
- PDF
STD: Sparse-to-Dense 3D Object Detector for Point Cloud
- Computer Science
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- 2019
- 129
- PDF