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Differentiable Visual Computing for Inverse Problems and Machine
  Learning

Differentiable Visual Computing for Inverse Problems and Machine Learning

21 November 2023
Andrew Spielberg
Fangcheng Zhong
Konstantinos Rematas
Krishna Murthy Jatavallabhula
Cengiz Öztireli
Tzu-Mao Li
Derek Nowrouzezahrai
ArXivPDFHTML

Papers citing "Differentiable Visual Computing for Inverse Problems and Machine Learning"

9 / 9 papers shown
Title
Learning Image Fractals Using Chaotic Differentiable Point Splatting
Adarsh Djeacoumar
Felix Mujkanovic
Hans-Peter Seidel
Thomas Leimkuhler
31
0
0
24 Feb 2025
2D Neural Fields with Learned Discontinuities
2D Neural Fields with Learned Discontinuities
Chenxi Liu
Siqi Wang
Matthew Fisher
Deepali Aneja
Alec Jacobson
3DH
32
1
0
15 Jul 2024
Solving Vision Tasks with Simple Photoreceptors Instead of Cameras
Solving Vision Tasks with Simple Photoreceptors Instead of Cameras
Andrei Atanov
Jiawei Fu
Rishubh Singh
Isabella Yu
Andrew Spielberg
Amir Zamir
31
1
0
17 Jun 2024
Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering
Dr.Jit: A Just-In-Time Compiler for Differentiable Rendering
Wenzel Jakob
Sébastien Speierer
Nicolas Roussel
Delio Vicini
27
34
0
02 Feb 2022
Differentiable Spline Approximations
Differentiable Spline Approximations
Minsu Cho
Aditya Balu
Ameya Joshi
Anjana Prasad
Biswajit Khara
S. Sarkar
Baskar Ganapathysubramanian
A. Krishnamurthy
C. Hegde
15
4
0
04 Oct 2021
Contact and Human Dynamics from Monocular Video
Contact and Human Dynamics from Monocular Video
Davis Rempe
Leonidas J. Guibas
Aaron Hertzmann
Bryan C. Russell
Ruben Villegas
Jimei Yang
3DH
65
99
0
22 Jul 2020
Neural Sparse Voxel Fields
Neural Sparse Voxel Fields
Lingjie Liu
Jiatao Gu
Kyaw Zaw Lin
Tat-Seng Chua
Christian Theobalt
180
1,234
0
22 Jul 2020
PointNet: Deep Learning on Point Sets for 3D Classification and
  Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
C. Qi
Hao Su
Kaichun Mo
Leonidas J. Guibas
3DH
3DPC
3DV
PINN
219
13,886
0
02 Dec 2016
Geometric deep learning on graphs and manifolds using mixture model CNNs
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti
Davide Boscaini
Jonathan Masci
Emanuele Rodolà
Jan Svoboda
M. Bronstein
GNN
234
1,801
0
25 Nov 2016
1