SOTAVerified

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

Papers

Showing 261270 of 272 papers

TitleStatusHype
Extracting Contact and Motion from Manipulation Videos0
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic SegmentationCode0
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data ProcessingCode0
Point cloud segmentation using hierarchical tree for architectural models0
RGCNN: Regularized Graph CNN for Point Cloud SegmentationCode0
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point CloudsCode0
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving0
Dynamic Graph CNN for Learning on Point CloudsCode1
Traffic Sign Timely Visual Recognizability Evaluation Based on 3D Measurable Point Clouds0
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric SpaceCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13Unverified
3PointNet++mean Corruption Error (mCE)1.11Unverified
4PointTransformersmean Corruption Error (mCE)1.05Unverified
5PointMLPmean Corruption Error (mCE)0.98Unverified
6PointMAEmean Corruption Error (mCE)0.93Unverified
7GDANetmean Corruption Error (mCE)0.92Unverified
8GDANetmean Corruption Error (mCE)0.89Unverified