GraVoS: Voxel Selection for 3D Point-Cloud Detection
Not on the map. Why?
Abstract (arXiv)
3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance. A common approach is to add ground-truth objects from other scenes. Differently, we propose to modify the scenes by removing elements (voxels), rather than adding ones. Our approach selects the "meaningful" voxels, in a manner that addresses both types of dataset imbalance. The approach is general and can be applied to any voxel-based detector, yet the meaningfulness of a voxel is network-dependent. Our voxel selection is shown to improve the performance of several prominent 3D detection methods.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 35 of its 47 references that are in the map. First posted on arXiv 18 Aug 2022.
Builds on
35 of 47 references.
References · 35
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
- 2018PointPillars: Fast Encoders for Object Detection from Point Cloudscited by 1,707
- 2017VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detectioncited by 1,323
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23 more
- 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
- 20203DSSD: Point-based 3D Single Stage Object Detectorcited by 440
- 2020Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detectioncited by 409
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- 2019PIXOR: Real-time 3D Object Detection from Point Cloudscited by 358
- 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
- 2017Joint 3D Proposal Generation and Object Detection from View Aggregationcited by 325
- 2019STD: Sparse-to-Dense 3D Object Detector for Point Cloudcited by 282
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- 2020Multi-Task Multi-Sensor Fusion for 3D Object Detectioncited by 203
- 2019Adaptive Gradient Methods with Dynamic Bound of Learning Ratecited by 181
- 2019Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detectioncited by 124
- 2019Fast Point R-CNNcited by 119
- 2021SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloudcited by 119
- 2019Imbalance Problems in Object Detection: A Reviewcited by 118
- 2020CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloudcited by 107
- 2020HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detectioncited by 82
- 2020HDNET: Exploiting HD Maps for 3D Object Detectioncited by 78
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- 2019Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimationcited by 31
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Citing papers per year: 2023: 2; 2024: 0; 2025: 1.
3 papers.