Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric
  Attention
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}.
6863479d588cea0da970c86f
Weida Wang
claimed_verified
2025-07-01T10:14:03.221Z
661b9d96c153e4a0a25adc3e
https://cdn-avatars.huggingface.co/v1/production/uploads/661b9d96c153e4a0a25adc3e/VRt7kCQ0KdJp-lhPLOajO.jpeg
Weida Wang
user
weidawang
6863479d588cea0da970c870
Changyong He
6863479d588cea0da970c871
Jin Zeng
claimed_verified
2025-07-02T07:41:00.694Z
6778eb379590be2fa48b1a73
/avatars/82209b2f70423bf26ea815f638707d21.svg
Helen Zeng
user
hushhushhush
6863479d588cea0da970c872
Di Qiu
Consistent Time-of-Flight Depth Denoising via Graph-Informed Geometric
  Attention
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}.
A ToF depth denoising network using motion-invariant graph fusion enhances temporal stability and spatial sharpness, achieving state-of-the-art performance on synthetic and real datasets.
ToF depth denoising motion-invariant graph fusion cross-frame geometric attention image smoothness prior data fidelity term maximum a posterior problem iterative filters graph-informed geometric attention