# Point Cloud Segmentation

90 papers with code • 1 benchmarks • 2 datasets

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.

## Libraries

Use these libraries to find Point Cloud Segmentation models and implementations## Most implemented papers

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

Point cloud is an important type of geometric data structure.

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

By exploiting metric space distances, our network is able to learn local features with increasing contextual scales.

# Point Transformer

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

# Dynamic Graph CNN for Learning on Point Clouds

Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices.

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

Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds.

# Stratified Transformer for 3D Point Cloud Segmentation

In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.

# Fast 3D Line Segment Detection From Unorganized Point Cloud

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud.

# Deep Learning for 3D Point Clouds: A Survey

To stimulate future research, this paper presents a comprehensive review of recent progress in deep learning methods for point clouds.

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

Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.

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

GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components.