859 citations · 940 across the 4 of their papers we have counts for
6 papers
Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations
Peng-Shuai Wang, Yang Liu, Xin Tong
We present an adaptive deep representation of volumetric fields of 3D shapes and an efficient approach to learn this deep representation for high-quality 3D shape reconstruction an…
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
Chun-Yu Sun, Yu-Qi Yang, Hao-Xiang Guo +4
The lack of fine-grained 3D shape segmentation data is the main obstacle to developing learning-based 3D segmentation techniques. We propose an effective semi-supervised method for…
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…
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…
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo +2
We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis. Built upon the octree representation of 3D shapes, our method takes the average normal v…