Point-to-Voxel Knowledge Distillation for LiDAR Semantic Segmentation (CVPR 2022) - cardwing/Codes-for-PVKD
最后,根据Voxel-based Set Attention (VSA),作者提出了一个VoxSeT,将VFE替换成VSA,后面部分全部采用的PointPillar的结构,包括:Point-to-BEV,2D CNN以及检测头。(此处仅介绍了一阶段模型) 结果 Waymo上的结果 kitti上的结果 参考文献 [1] He, C., Li, R., Li, S., & Zhang, L. (2022). Voxel Set ...
To address this limitation, we propose PVTransformer: a transformer-based point-to-voxel architecture for 3D detection. Our key idea is to replace the PointNet pooling operation with an attention module, leading to a better point-to-voxel aggregation function. Our design respects the permutation ...
The window-based point-voxel branch concentrates on local feature learning while integrating voxel-level information within each window. This unique design enables the model to jointly extract local details and regional structures efficiently and provides an effective and efficient solution for multi-scale...
Regional-to-Local Point-Voxel Transformer (RegionPVT) This is the official PyTorch implementation of the paper Regional-to-Local Point-Voxel Transformer for Large-scale Indoor 3D Point Cloud Semantic Segmentation Get Started Environment Install dependencies pip install -r requirements.txt Compile point...
Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds (CVPR 2022) - skyhehe123/VoxSeT
Exploratory analysis of voxel size effects on CT measurements of situation characteristics of type point-to-point distanceIn recent years, computed tomography (CT) has been applied as an industrial metrology tool for the dimensional evaluation of visible and even hidden features of production parts in...
The most recent 3D object detectors for point clouds rely on the coarse voxel-based representation rather than the accurate point-based representation due to a higher box recall in the voxel-based Region Proposal Network (RPN). However, the detection performance is severely restricted by the inform...
Describe the bug I have a point cloud, which is filtered by the voxel Filter. The resulting point cloud differs A LOT from the original point cloud. This happens not always but only happens when the scene is changing very fast. Context I...
a transformer-based point-to-voxel architecture for 3D detection. Our key idea is to replace the PointNet pooling operation with an attention module, leading to a better point-to-voxel aggregation function. Our design respects the permutation invariance of sparse 3D points while being more expressiv...