activity
20192022
most citedMultimodal Shape Completion via Conditional Generative Adversarial Networks

6 citations · 8 across the 4 of their papers we have counts for

collaborators

6 papers

cs.CV2022★ 2 cited

Visual Localization via Few-Shot Scene Region Classification

Siyan Dong, Shuzhe Wang, Yixin Zhuang +3

Visual (re)localization addresses the problem of estimating the 6-DoF (Degree of Freedom) camera pose of a query image captured in a known scene, which is a key building block of m…

cs.CV2022

A Simple And Effective Filtering Scheme For Improving Neural Fields

Yixin Zhuang

Recently, neural fields, also known as coordinate-based MLPs, have achieved impressive results in representing low-dimensional data. Unlike CNN, MLPs are globally connected and lac…

cs.CV2021

Neural Implicit 3D Shapes from Single Images with Spatial Patterns

Yixin Zhuang, Yunzhe Liu, Yujie Wang +1

Neural implicit functions have achieved impressive results for reconstructing 3D shapes from single images. However, the image features for describing 3D point samplings of implici…

cs.CV2020★ 6 cited

Multimodal Shape Completion via Conditional Generative Adversarial Networks

Rundi Wu, Xuelin Chen, Yixin Zhuang +1

Several deep learning methods have been proposed for completing partial data from shape acquisition setups, i.e., filling the regions that were missing in the shape. These methods,…

cs.CV2019

Decoupling Features and Coordinates for Few-shot RGB Relocalization

Siyan Dong, Songyin Wu, Yixin Zhuang +3

Cross-scene model adaption is crucial for camera relocalization in real scenarios. It is often preferable that a pre-learned model can be fast adapted to a novel scene with as few…

cs.CV2019

PQ-NET: A Generative Part Seq2Seq Network for 3D Shapes

Rundi Wu, Yixin Zhuang, Kai Xu +2

We introduce PQ-NET, a deep neural network which represents and generates 3D shapes via sequential part assembly. The input to our network is a 3D shape segmented into parts, where…