Search Results for author: Peng-Shuai Wang

Found 19 papers, 16 papers with code

SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images

1 code implementation18 Dec 2023 Guo Pu, Peng-Shuai Wang, Zhouhui Lian

This paper proposes SinMPI, a novel method that uses an expanded multiplane image (MPI) as the 3D scene representation to significantly expand the perspective range of MPI and generate high-quality novel views from a large multiplane space.

Novel View Synthesis

Learning the Geodesic Embedding with Graph Neural Networks

1 code implementation11 Sep 2023 Bo Pang, Zhongtian Zheng, Guoping Wang, Peng-Shuai Wang

Then, we can compute the geodesic distance between a pair of points using our decoding function, which requires only several matrix multiplications and can be massively parallelized on GPUs.

OctFormer: Octree-based Transformers for 3D Point Clouds

3 code implementations4 May 2023 Peng-Shuai Wang

To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window.

3D Object Detection 3D Semantic Segmentation +2

Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding

2 code implementations14 Apr 2023 Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong, Yang Liu, Hao Pan, Peng-Shuai Wang, Xin Tong, Baining Guo

We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset.

Ranked #2 on 3D Object Detection on S3DIS (using extra training data)

3D Object Detection Scene Understanding +1

3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining

no code implementations14 Apr 2023 Siming Yan, YuQi Yang, YuXiao Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, QiXing Huang

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.


Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning

1 code implementation ICCV 2023 Huimin Wu, Chenyang Lei, Xiao Sun, Peng-Shuai Wang, Qifeng Chen, Kwang-Ting Cheng, Stephen Lin, Zhirong Wu

Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part.

Data Augmentation Quantization +2

SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation

1 code implementation24 Jun 2022 Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, Xin Tong

We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes.

3D Generation 3D Shape Generation +1

Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations

1 code implementation5 May 2022 Peng-Shuai Wang, Yang Liu, Xin Tong

Our method encodes the volumetric field of a 3D shape with an adaptive feature volume organized by an octree and applies a compact multilayer perceptron network for mapping the features to the field value at each 3D position.

3D Shape Reconstruction Decoder

Semi-supervised 3D shape segmentation with multilevel consistency and part substitution

1 code implementation19 Apr 2022 Chun-Yu Sun, Yu-Qi Yang, Hao-Xiang Guo, Peng-Shuai Wang, Xin Tong, Yang Liu, Heung-Yeung Shum

We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data.

Segmentation Semantic Segmentation +2

Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks

1 code implementation ICCV 2021 Yu-Qi Yang, Peng-Shuai Wang, Yang Liu

For fine-grained 3D vision tasks where point-wise features are essential, like semantic segmentation and 3D detection, our network achieves higher prediction accuracy than the existing networks using the nearest neighbor interpolation or the normalized trilinear interpolation with the zero-padding or the octree-padding scheme.

Segmentation Semantic Segmentation

Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields

1 code implementation3 Jun 2021 Peng-Shuai Wang, Yang Liu, Yu-Qi Yang, Xin Tong

Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or occupancy values.

3D Shape Reconstruction Image Reconstruction

Deep Implicit Moving Least-Squares Functions for 3D Reconstruction

1 code implementation CVPR 2021 Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu

We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces.

3D Object Reconstruction 3D Reconstruction +1

Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination

1 code implementation3 Aug 2020 Peng-Shuai Wang, Yu-Qi Yang, Qian-Fang Zou, Zhirong Wu, Yang Liu, Xin Tong

Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.

3D Point Cloud Linear Classification 3D Semantic Segmentation

Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion

1 code implementation6 Jun 2020 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.

Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes

1 code implementation21 Sep 2018 Peng-Shuai Wang, Chun-Yu Sun, Yang Liu, Xin Tong

The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant.


Mesh Denoising via Cascaded Normal Regression

no code implementations15 Nov 2016 Peng-Shuai Wang, Yang Liu, Xin Tong

At runtime, our method applies the learned cascaded regression functions to a noisy input mesh and reconstructs the denoised mesh from the output facet normals.

Denoising regression

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