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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–272 of 272 papers

TitleStatusHype
Point Cloud Segmentation Using Sparse Temporal Local Attention—0
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—0
PointNLM: Point Nonlocal-Means for vegetation segmentation based on middle echo point clouds—0
PointResNet: Residual Network for 3D Point Cloud Segmentation and Classification—0
Point-to-Pose Voting based Hand Pose Estimation using Residual Permutation Equivariant Layer—0
POIRot: A rotation invariant omni-directional pointnet—0
ProtoGuard-guided PROPEL: Class-Aware Prototype Enhancement and Progressive Labeling for Incremental 3D Point Cloud Segmentation—0
ProtoTransfer: Cross-Modal Prototype Transfer for Point Cloud Segmentation—0
Provable Adversarial Robustness for Group Equivariant Tasks: Graphs, Point Clouds, Molecules, and More—0
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage—0
Pyramid Architecture for Multi-Scale Processing in Point Cloud Segmentation—0
Refining Segmentation On-the-Fly: An Interactive Framework for Point Cloud Semantic Segmentation—0
Region-Transformer: Self-Attention Region Based Class-Agnostic Point Cloud Segmentation—0
Revisiting 2D Convolutional Neural Networks for Graph-based Applications—0
RiEMann: Near Real-Time SE(3)-Equivariant Robot Manipulation without Point Cloud Segmentation—0
Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks—0
RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation—0
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization—0
SAT: Size-Aware Transformer for 3D Point Cloud Semantic Segmentation—0
Scale Disparity of Instances in Interactive Point Cloud Segmentation—0
Superpoint-guided Semi-supervised Semantic Segmentation of 3D Point Clouds—0
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data—0
Show:102550
← PrevPage 6 of 6Next →

Benchmark Results

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