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 101110 of 272 papers

TitleStatusHype
Oriented Point Sampling for Plane Detection in Unorganized Point CloudsCode0
PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic SegmentationCode0
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data ProcessingCode0
LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable TerrainCode0
MmWave Radar Point Cloud Segmentation using GMM in Multimodal Traffic MonitoringCode0
A Dataset for Analysing Complex Document Layouts in the Digital Humanities and Its Evaluation with Krippendorff’s AlphaCode0
3D-model ShapeNet Core Classification using Meta-Semantic LearningCode0
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D ImagesCode0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point CloudsCode0
Show:102550
← PrevPage 11 of 28Next →

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