Related papers: $\nabla$-SDF: Learning Euclidean Signed Distance Functions Online with Gradient-Augmented Octree Interpolation and Neural Residual

$\nabla$-SDF: Learning Euclidean Signed Distance Functions Online with Gradient-Augmented Octree Interpolation and Neural Residual

URL: http://arxiv.org/abs/2510.18999v1
Date: Tue, 21 Oct 2025 18:24:45 GMT
Title: $\nabla$-SDF: Learning Euclidean Signed Distance Functions Online with Gradient-Augmented Octree Interpolation and Neural Residual
Authors: Zhirui Dai, Qihao Qian, Tianxing Fan, Nikolay Atanasov,
Abstract summary: $nabla$-SDF is a hybrid method that combines an explicit prior obtained from gradient-augmented octree with an implicit neural residual.<n>Our method achieves non-truncated SDF reconstruction with computational and memory efficiency comparable to methods and differentiability and accuracy comparable to neural network methods.
Score: 6.772832523044964
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Estimation of signed distance functions (SDFs) from point cloud data has been shown to benefit many robot autonomy capabilities, including localization, mapping, motion planning, and control. Methods that support online and large-scale SDF reconstruction tend to rely on discrete volumetric data structures, which affect the continuity and differentiability of the SDF estimates. Recently, using implicit features, neural network methods have demonstrated high-fidelity and differentiable SDF reconstruction but they tend to be less efficient, can experience catastrophic forgetting and memory limitations in large environments, and are often restricted to truncated SDFs. This work proposes $\nabla$-SDF, a hybrid method that combines an explicit prior obtained from gradient-augmented octree interpolation with an implicit neural residual. Our method achieves non-truncated (Euclidean) SDF reconstruction with computational and memory efficiency comparable to volumetric methods and differentiability and accuracy comparable to neural network methods. Extensive experiments demonstrate that \methodname{} outperforms the state of the art in terms of accuracy and efficiency, providing a scalable solution for downstream tasks in robotics and computer vision.

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