Physics Based Differentiable Rendering for Inverse Problems and Beyond

TL;DR

Physics-based differentiable rendering (PBDR) is a powerful approach in computer vision and graphics that models light and material interactions for solving inverse problems, with applications in autonomous navigation, scene reconstruction, and material design.

Contribution

This paper provides an extensive overview of PBDR techniques, highlighting their development, effectiveness, and limitations in inverse problem solving.

Findings

PBDR effectively models physical light interactions.

Modern PBDR techniques enhance scene reconstruction accuracy.

Limitations include computational complexity and model assumptions.

Abstract

Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by adding physical models of light propagation and materials interaction. Due to these capabilities, distinguished rendering has been employed in a wider range of sectors such as autonomous navigation, scene reconstruction, and material design. We provide an extensive overview of PBDR techniques in this study, emphasizing their creation, effectiveness, and limitations while managing inverse situations. We demonstrate modern techniques and examine their value in everyday situations.