Unsupervised point cloud representation learning with deep neural networks: A survey
Point cloud data have been widely explored due to its superior accuracy and robustness
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
under various adverse situations. Meanwhile, deep neural networks (DNNs) have achieved …
Surface form inspection with contact coordinate measurement: a review
Parts with high-quality freeform surfaces have been widely used in industries, which require
strict quality control during the manufacturing process. Among all the industrial inspection …
strict quality control during the manufacturing process. Among all the industrial inspection …
Pufa-gan: A frequency-aware generative adversarial network for 3d point cloud upsampling
We propose a generative adversarial network for point cloud upsampling, which can not
only make the upsampled points evenly distributed on the underlying surface but also …
only make the upsampled points evenly distributed on the underlying surface but also …
Pointmixer: Mlp-mixer for point cloud understanding
MLP-Mixer has newly appeared as a new challenger against the realm of CNNs and
Transformer. Despite its simplicity compared to Transformer, the concept of channel-mixing …
Transformer. Despite its simplicity compared to Transformer, the concept of channel-mixing …
Learning a more continuous zero level set in unsigned distance fields through level set projection
Latest methods represent shapes with open surfaces using unsigned distance functions
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
(UDFs). They train neural networks to learn UDFs and reconstruct surfaces with the …
Grad-pu: Arbitrary-scale point cloud upsampling via gradient descent with learned distance functions
Most existing point cloud upsampling methods have roughly three steps: feature extraction,
feature expansion and 3D coordinate prediction. However, they usually suffer from two …
feature expansion and 3D coordinate prediction. However, they usually suffer from two …
Hyperbolic chamfer distance for point cloud completion
Chamfer distance (CD) is a standard metric to measure the shape dissimilarity between
point clouds in point cloud completion, as well as a loss function for (deep) learning …
point clouds in point cloud completion, as well as a loss function for (deep) learning …
Patchformer: An efficient point transformer with patch attention
The point cloud learning community is witnesses a modeling shift from CNNs to
Transformers, where pure Transformer architectures have achieved top accuracy on the …
Transformers, where pure Transformer architectures have achieved top accuracy on the …
Pu-transformer: Point cloud upsampling transformer
Given the rapid development of 3D scanners, point clouds are becoming popular in AI-
driven machines. However, point cloud data is inherently sparse and irregular, causing …
driven machines. However, point cloud data is inherently sparse and irregular, causing …
IterativePFN: True iterative point cloud filtering
The quality of point clouds is often limited by noise introduced during their capture process.
Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud …
Consequently, a fundamental 3D vision task is the removal of noise, known as point cloud …