Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric
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Authors: name: Weida Wang, name: Changyong He, name: Jin Zeng, name: Di Qiu

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
https://github.com/davidweidawang/GIGA-ToF{https://github.com/davidweidawang/GIGA-ToF}.