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Found 4 papers, 1 papers with code
Enhanced Prototypical Learning for Unsupervised Domain Adaptation in LiDAR Semantic Segmentation
We evaluate the performance of our method on the recently proposed LiDAR segmentation UDA scenarios.
Projection-based Point Convolution for Efficient Point Cloud Segmentation
Point branch consists of MLPs, while projection branch transforms point features into a 2D feature map and then apply 2D convolutions.
Progressive Seed Generation Auto-encoder for Unsupervised Point Cloud Learning
With the development of 3D scanning technologies, 3D vision tasks have become a popular research area.
Ranked #8 on
3D Point Cloud Linear Classification
on ModelNet40
3D Point Cloud Linear Classification
Point cloud reconstruction
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation
In this paper, we propose a simple and efficient architecture named point projection and back-projection network (PBP-Net), which leverages 2D CNNs for the 3D point cloud segmentation.
