Cloud Point
https://github.com/nicolas-chaulet/torch-points3d
Unsupversed
Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning,ICCV19
Task Unsupervised denoising: Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.
Unsupervised Multi-Task Feature Learning on Point Clouds,ICCV19
Self-supervised Modal and View Invariant Feature Learning,Arxiv2005
PointContrast: Unsupervised Pre-training for 3D Point Cloud Understanding,ECCV20,spotlight
Mapping in a Cycle: Sinkhorn Regularized Unsupervised Learning for Point Cloud Shapes,ECCV20
Self-Supervised Learning of Point Clouds via Orientation Estimation,Arxiv2008
Point Cloud Augmentation
PointAugment: an Auto-Augmentation Framework for Point Cloud Classification,CVPR20,oral
Point cloud classification
Point Sampling
The input to S-NET is a set of n 3D coordinates, namely points, representing a 3D shape. The output of S-NET is k generated points.
Detail structure of S-Net can be seen in A.1, check code.
G maybe is not a subset P, do matching process by KNN.
construct a sampling regularization loss,composed out of three terms:
\[L_{f}(G,P) = \frac{1}{|G|} \sum_{g \in G} min_{p \in P} ||p - g||^{2}_{2}\] \[L_{b}(G,P) = \frac{1}{|P|} \sum_{p \in P} min_{G \in G} ||p - g||^{2}_{2}\] \[L_{m}(G,P) = max_{g \in G} min_{p \in P} ||g-p||^{2}_{2}\]\(L_{f}(G,P) + L_{b}(G,P)\) is a typical Chamfer Distance(CD).
SampleNet: Differentiable Point Cloud Sampling,CVPR20
extend previous works by introducing a differentiable relaxation to the matching step, i.e., nearest neighbor selection, during training.
Temperature in softmax is learnable, counter-intuitive.