NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation

12 June 2022

Papers citing "NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation"

8 / 8 papers shown

Title
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations Tristan Aumentado-Armstrong Ashkan Mirzaei Marcus A. Brubaker J. Kelly Alex Levinshtein Konstantinos G. Derpanis Igor Gilitschenski 35 4 0 27 Oct 2023
OmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction Jiaming Shen Bo-Yue Song Zirui Wu Yi Xu AI4CE 26 2 0 27 Sep 2022
FIRe: Fast Inverse Rendering using Directional and Signed Distance Functions Tarun Yenamandra A. Tewari Nan Yang Florian Bernard Christian Theobalt Daniel Cremers 16 7 0 30 Mar 2022
ShapeFormer: Transformer-based Shape Completion via Sparse Representation Xingguang Yan Liqiang Lin Niloy J. Mitra Dani Lischinski Daniel Cohen-Or Hui Huang ViT 64 112 0 25 Jan 2022
Curriculum DeepSDF Yueqi Duan Haidong Zhu He-Nan Wang L. Yi Ram Nevatia Leonidas J. Guibas 70 94 0 19 Mar 2020
Convolutional Occupancy Networks Songyou Peng Michael Niemeyer L. Mescheder Marc Pollefeys Andreas Geiger 3DV AI4CE 232 971 0 10 Mar 2020
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 222 14,103 0 02 Dec 2016
Geometric deep learning: going beyond Euclidean data M. Bronstein Joan Bruna Yann LeCun Arthur Szlam P. Vandergheynst GNN 253 3,239 0 24 Nov 2016