859 citations · 1.2k across the 11 of their papers we have counts for
11 papers
6G Network Business Support System
Ye Ouyang, Yaqin Zhang, Peng Wang +7
6G is the next-generation intelligent and integrated digital information infrastructure, characterized by ubiquitous interconnection, native intelligence, multi-dimensional percept…
Locally Attentional SDF Diffusion for Controllable 3D Shape Generation
Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang +3
Although the recent rapid evolution of 3D generative neural networks greatly improves 3D shape generation, it is still not convenient for ordinary users to create 3D shapes and con…
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Siming Yan, Yuqi Yang, Yuxiao Guo +5
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used i…
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong +5
The use of pretrained backbones with fine-tuning has been successful for 2D vision and natural language processing tasks, showing advantages over task-specific networks. In this wo…
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang +1
We present a StyleGAN2-based deep learning approach for 3D shape generation, called SDF-StyleGAN, with the aim of reducing visual and geometric dissimilarity between generated shap…
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…