DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited

• URL: http://arxiv.org/abs/2604.10377v1
• Date: Sat, 11 Apr 2026 23:15:07 GMT
• Title: DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited
• Abstract summary: We find that standard functional map implementations solve k independent linear systems serially, which is a computational bottleneck at higher spectral resolution.<n>We propose a vectorized reformulation that solves all systems in a single kernel call, achieving up to a 33x speedup.<n>We present an open-source, DeepShapeMatchingKit, that incorporates these improvements and standardizes training, evaluation, and data pipelines for common deep shape matching methods.
• Score: 48.627686484608525
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep functional maps, leveraging learned feature extractors and spectral correspondence solvers, are fundamental to non-rigid 3D shape matching. Based on an analysis of open-source implementations, we find that standard functional map implementations solve k independent linear systems serially, which is a computational bottleneck at higher spectral resolution. We thus propose a vectorized reformulation that solves all systems in a single kernel call, achieving up to a 33x speedup while preserving the exact solution. Furthermore, we identify and document a previously unnoticed implementation divergence in the spatial gradient features of the mainstay DiffusionNet: two variants that parameterize distinct families of tangent-plane transformations, and present experiments analyzing their respective behaviors across diverse benchmarks. We additionally revisit overlap prediction evaluation for partial-to-partial matching and show that balanced accuracy provides a useful complementary metric under varying overlap ratios. To share these advancements with the wider community, we present an open-source codebase, DeepShapeMatchingKit, that incorporates these improvements and standardizes training, evaluation, and data pipelines for common deep shape matching methods. The codebase is available at: https://github.com/xieyizheng/DeepShapeMatchingKit

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