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20182021
most citedExtending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers

18 citations · 82 across the 15 of their papers we have counts for

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21 papers · 1 filter

cs.CV20211 cited

DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates

Minghua Liu, Minhyuk Sung, Radomir Mech +1

We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points…

cs.CV2021

GNeRF: GAN-based Neural Radiance Field without Posed Camera

Quan Meng, Anpei Chen, Haimin Luo +5

We introduce GNeRF, a framework to marry Generative Adversarial Networks (GAN) with Neural Radiance Field (NeRF) reconstruction for the complex scenarios with unknown and even rand…

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.CV20213 cited

NeuTex: Neural Texture Mapping for Volumetric Neural Rendering

Fanbo Xiang, Zexiang Xu, Miloš Hašan +3

Recent work has demonstrated that volumetric scene representations combined with differentiable volume rendering can enable photo-realistic rendering for challenging scenes that me…

cs.CV2020

Semantically Robust Unpaired Image Translation for Data with Unmatched Semantics Statistics

Zhiwei Jia, Bodi Yuan, Kangkang Wang +4

Many applications of unpaired image-to-image translation require the input contents to be preserved semantically during translations. Unaware of the inherently unmatched semantics…

cs.CV20207 cited

Compositionally Generalizable 3D Structure Prediction

Songfang Han, Jiayuan Gu, Kaichun Mo +4

Single-image 3D shape reconstruction is an important and long-standing problem in computer vision. A plethora of existing works is constantly pushing the state-of-the-art performan…