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The paper introduces Iskra, a system for automatically differentiating through geometry processing algorithms implemented using imperative code. Iskra leverages the adjoint method and scatter-gather mesh processing to efficiently compute gradients for algorithms using local-global and ADMM solvers. The system enables inverse geometry processing applications by providing a low-effort, fast, and memory-efficient alternative to generic differentiable optimization.
Differentiate through your existing geometry processing code with low overhead thanks to Iskra, a system that automatically generates efficient backward passes.
We propose a system for differentiating through solutions to geometry processing problems. Our system differentiates a broad class of geometric algorithms, exploiting existing fast problem-specific schemes common to geometry processing, including local-global and ADMM solvers. It is compatible with machine learning frameworks, opening doors to new classes of inverse geometry processing applications. We marry the scatter-gather approach to mesh processing with tensor-based workflows and rely on the adjoint method applied to user-specified imperative code to generate an efficient backward pass behind the scenes. We demonstrate our approach by differentiating through mean curvature flow, spectral conformal parameterization, geodesic distance computation, and as-rigid-as-possible deformation, examining usability and performance on these applications. Our system allows practitioners to differentiate through existing geometry processing algorithms without needing to reformulate them, resulting in low implementation effort, fast runtimes, and lower memory requirements than differentiable optimization tools not tailored to geometry processing.