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
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable TerrainCode0
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point CloudCode0
Leveraging Pre-Trained 3D Object Detection Models For Fast Ground Truth Generation0
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
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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