859 citations · 906 across the 5 of their papers we have counts for
5 papers
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…
Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images
Ming-Jia Yang, Yu-Xiao Guo, Bin Zhou +1
We present a method for creating 3D indoor scenes with a generative model learned from a collection of semantic-segmented depth images captured from different unknown scenes. Given…
View-volume Network for Semantic Scene Completion from a Single Depth Image
Yu-Xiao Guo, Xin Tong
We introduce a View-Volume convolutional neural network (VVNet) for inferring the occupancy and semantic labels of a volumetric 3D scene from a single depth image. The VVNet concat…
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…