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Found 5 papers, 2 papers with code
Learning SO(3)-Invariant Semantic Correspondence via Local Shape Transform
Establishing accurate 3D correspondences between shapes stands as a pivotal challenge with profound implications for computer vision and robotics.
Stable and Consistent Prediction of 3D Characteristic Orientation via Invariant Residual Learning
Learning to predict reliable characteristic orientations of 3D point clouds is an important yet challenging problem, as different point clouds of the same class may have largely varying appearances.
Fast Point Transformer
The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem.
Ranked #24 on
Semantic Segmentation
on S3DIS
PointMixer: MLP-Mixer for Point Cloud Understanding
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and transformer.
Ranked #19 on
Semantic Segmentation
on S3DIS Area5
Efficient Point Transformer for Large-scale 3D Scene Understanding
Although sparse convolution is efficient and scalable for large 3D scenes, the quantization artifacts impair geometric details and degrade prediction accuracy.
