activity
20182023
most citedHow You Act Tells a Lot: Privacy-Leakage Attack on Deep Reinforcement Learning

14 citations · 20 across the 4 of their papers we have counts for

collaborators

11 papers

cs.CV20233 cited

MovingParts: Motion-based 3D Part Discovery in Dynamic Radiance Field

Kaizhi Yang, Xiaoshuai Zhang, Zhiao Huang +3

We present MovingParts, a NeRF-based method for dynamic scene reconstruction and part discovery. We consider motion as an important cue for identifying parts, that all particles on…

cs.CV2023

TensoIR: Tensorial Inverse Rendering

Haian Jin, Isabella Liu, Peijia Xu +6

We propose TensoIR, a novel inverse rendering approach based on tensor factorization and neural fields. Unlike previous works that use purely MLP-based neural fields, thus sufferin…

cs.CV20223 cited

NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction

Xiaoshuai Zhang, Sai Bi, Kalyan Sunkavalli +2

While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale…

cs.CV2021

MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo

Anpei Chen, Zexiang Xu, Fuqiang Zhao +4

We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Unlike prior works on neural radiance fields that…

cs.CV2020

Meshing Point Clouds with Predicted Intrinsic-Extrinsic Ratio Guidance

Minghua Liu, Xiaoshuai Zhang, Hao Su

We are interested in reconstructing the mesh representation of object surfaces from point clouds. Surface reconstruction is a prerequisite for downstream applications such as rende…

cs.CV2019

Disentangled Image Matting

Shaofan Cai, Xiaoshuai Zhang, Haoqiang Fan +6

Most previous image matting methods require a roughly-specificed trimap as input, and estimate fractional alpha values for all pixels that are in the unknown region of the trimap.…