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

TL;DR

This paper introduces a novel ToF depth denoising network that uses graph-informed geometric attention to improve temporal stability and spatial sharpness, achieving state-of-the-art results.

Contribution

It proposes a graph fusion-based denoising method that leverages motion-invariant graph structures for enhanced temporal and spatial depth image quality.

Findings

Achieves state-of-the-art accuracy on synthetic datasets.

Demonstrates robust generalization on real Kinectv2 data.

Produces a high-performance, interpretable denoising network.

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}.