Point Cloud Segmentation

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation. Source: 3D point cloud segmentation: A survey

Benchmarks

Libraries

Datasets

Subtasks

Most implemented papers

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

Content

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

Point Transformer

Dynamic Graph CNN for Learning on Point Clouds

PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation

Fast 3D Line Segment Detection From Unorganized Point Cloud

Stratified Transformer for 3D Point Cloud Segmentation

Deep Learning for 3D Point Clouds: A Survey

Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud

SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation