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

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Showing 101–110 of 272 papers

TitleStatusHype
IPC-Net: 3D point-cloud segmentation using deep inter-point convolutional layers—0
GeoSpark: Sparking up Point Cloud Segmentation with Geometry Clue—0
Contrastive Learning for Self-Supervised Pre-Training of Point Cloud Segmentation Networks With Image Data—0
Joint 3D Point Cloud Segmentation using Real-Sim Loop: From Panels to Trees and Branches—0
Label Name is Mantra: Unifying Point Cloud Segmentation across Heterogeneous Datasets—0
Ground Awareness in Deep Learning for Large Outdoor Point Cloud Segmentation—0
Deep Learning Based 3D Segmentation: A Survey—0
LatticeNet: Fast Spatio-Temporal Point Cloud Segmentation Using Permutohedral Lattices—0
HIDA: Towards Holistic Indoor Understanding for the Visually Impaired via Semantic Instance Segmentation with a Wearable Solid-State LiDAR Sensor—0
FuseSeg: LiDAR Point Cloud Segmentation Fusing Multi-Modal Data—0
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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