Radiative Backpropagation for Differentiable Inverse Rendering
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Solution Overview
Problem
Current differentiable rendering techniques for inverse rendering are computationally expensive and memory-intensive due to the requirement of a transcript to record intermediate computation steps, making them infeasible for large and complex simulations.
Innovation Solution
The proposed method introduces radiative backpropagation, a novel technique that eliminates the need for a transcript by recasting backpropagation of derivatives through a rendering algorithm as the solution of a modified light simulation involving partial derivatives of radiance with respect to the optimisation objective.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional differentiable rendering techniques are used to perform inverse rendering, then derivatives can be obtained to update scene parameters, but memory requirements and computation time increase significantly due to the need to record intermediate computation steps in a transcript
Solution Approach 1:
The patent extracts and eliminates the transcript component from the rendering pipeline. By using adjoint rendering, derivatives are computed directly through a separate rendering pass without requiring intermediate computation steps to be stored, thereby removing the memory burden of the transcript while preserving derivative accuracy
Solution Approach 2:
The patent inverts the traditional differentiation approach by using adjoint rendering, which computes derivatives by rendering backwards from the image plane to the scene. This inversion allows derivative computation without forward-mode transcription, fundamentally changing how derivatives are obtained in differentiable rendering
2Measurement precision
If traditional differentiable rendering techniques are used to perform inverse rendering, then derivatives can be obtained to update scene parameters, but computation time increases due to the overhead of transcript recording and processing
Solution Approach 1:
The patent removes the transcript recording and processing steps from the rendering pipeline. By computing derivatives directly through adjoint rendering in a single backward pass, the method eliminates the time-consuming transcript operations while maintaining derivative accuracy
Solution Approach 2:
The patent creates an adjoint version of the rendering algorithm that mirrors the forward rendering process. This adjoint copy computes derivatives by propagating sensitivity information backwards through the rendering pipeline, providing an efficient alternative to transcript-based differentiation
3Reliability
If physically based rendering methods are used to simulate light transport and scattering effects, then photorealism is achieved, but rendering time increases to hours for a single image
Solution Approach 1:
The patent substitutes the traditional forward-light-transport mechanics with adjoint rendering mechanics. By computing derivatives through a backward propagation process rather than simulating light transport forward, the method maintains photorealistic quality while dramatically reducing computation time
Data Source
AI summary
A computer-implemented inverse rendering method comprising: computing an adjoint image by differentiating an objective function that evaluates the quality of a rendered image, image elements of the adjoint image encoding the sensitivity of spatially corresponding image elements of the rendered image with respect to the objective function; sampling the adjoint image at a respective sample position to determine a respective adjoint radiance value associated with the respective sample position; emitting the respective adjoint radiance value into a scene model characterised by scene parameters; determining an interaction location of a respective incident adjoint radiance value with a surface and/or volume of the scene model; determining a respective incident radiance value or an approximation thereof at the interaction location; and updating a scene parameter gradient.