Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention

• URL: http://arxiv.org/abs/2506.23542v1
• Date: Mon, 30 Jun 2025 06:29:24 GMT
• Title: Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric Attention
• Authors: Weida Wang, Changyong He, Jin Zeng, Di Qiu,
• Abstract summary: We propose a novel ToF depth denoising network leveraging motion-invariant graph fusion.<n>Despite depth shifts across frames, graph structures exhibit temporal self-similarity, enabling cross-frame geometric attention for graph fusion.<n>The proposed scheme achieves state-of-the-art performance in terms of accuracy and consistency on synthetic DVToF dataset and exhibits robust generalization on the real Kinectv2 dataset.
• Score: 5.196236145367301
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth images captured by Time-of-Flight (ToF) sensors are prone to noise, requiring denoising for reliable downstream applications. Previous works either focus on single-frame processing, or perform multi-frame processing without considering depth variations at corresponding pixels across frames, leading to undesirable temporal inconsistency and spatial ambiguity. In this paper, we propose a novel ToF depth denoising network leveraging motion-invariant graph fusion to simultaneously enhance temporal stability and spatial sharpness. Specifically, despite depth shifts across frames, graph structures exhibit temporal self-similarity, enabling cross-frame geometric attention for graph fusion. Then, by incorporating an image smoothness prior on the fused graph and data fidelity term derived from ToF noise distribution, we formulate a maximum a posterior problem for ToF denoising. Finally, the solution is unrolled into iterative filters whose weights are adaptively learned from the graph-informed geometric attention, producing a high-performance yet interpretable network. Experimental results demonstrate that the proposed scheme achieves state-of-the-art performance in terms of accuracy and consistency on synthetic DVToF dataset and exhibits robust generalization on the real Kinectv2 dataset. Source code will be released at \href{https://github.com/davidweidawang/GIGA-ToF}{https://github.com/davidweidawang/GIGA-ToF}.

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