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
Impact of color and mixing proportion of synthetic point clouds on semantic segmentationCode0
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud SegmentationCode0
SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point CloudCode0
DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance FieldsCode0
cilantro: A Lean, Versatile, and Efficient Library for Point Cloud Data ProcessingCode0
Scalable Certified Segmentation via Randomized SmoothingCode0
Zero-shot point cloud segmentation by transferring geometric primitivesCode0
GSTran: Joint Geometric and Semantic Coherence for Point Cloud SegmentationCode0
Boundary-Aware Geometric Encoding for Semantic Segmentation of Point CloudsCode0
Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental ModelCode0
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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