8 citations · 18 across the 3 of their papers we have counts for
5 papers
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
Shi-Lin Liu, Hao-Xiang Guo, Hao Pan +3
Point set is a flexible and lightweight representation widely used for 3D deep learning. However, their discrete nature prevents them from representing continuous and fine geometry…
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou +3
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learnin…
Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion
Peng-Shuai Wang, Yang Liu, Xin Tong
Acquiring complete and clean 3D shape and scene data is challenging due to geometric occlusion and insufficient views during 3D capturing. We present a simple yet effective deep le…
OoDAnalyzer: Interactive Analysis of Out-of-Distribution Samples
Changjian Chen, Jun Yuan, Yafeng Lu +4
One major cause of performance degradation in predictive models is that the test samples are not well covered by the training data. Such not well-represented samples are called OoD…
Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes
Peng-Shuai Wang, Chun-Yu Sun, Yang Liu +1
We present an Adaptive Octree-based Convolutional Neural Network (Adaptive O-CNN) for efficient 3D shape encoding and decoding. Different from volumetric-based or octree-based CNN…