OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping

[Submitted on 21 Oct 2025 (v1), last revised 8 Jul 2026 (this version, v3)]

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Abstract:Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mapping, motion planning, and control. Methods that support online and large-scale SDF reconstruction often rely on discrete volumetric data structures, which affects the continuity and differentiability of the SDF estimates. Neural network methods have demonstrated high-fidelity differentiable SDF reconstruction but they tend to be less efficient, experience catastrophic forgetting and memory limitations in large environments, and are often restricted to truncated SDF. This work proposes OREN, a hybrid method that combines an explicit prior from octree interpolation with an implicit residual from neural network regression. 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 OREN outperforms the state of the art in terms of accuracy and efficiency, providing a scalable solution for downstream tasks in robotics and computer vision.

Submission history

From: Zhirui Dai [view email]
[v1] Tue, 21 Oct 2025 18:24:45 UTC (12,786 KB)
[v2] Fri, 24 Apr 2026 01:31:40 UTC (16,568 KB)
[v3] Wed, 8 Jul 2026 20:16:22 UTC (20,300 KB)