Towards Robust Time-of-Flight Depth Denoising with Confidence-Aware Diffusion Model
This addresses noise issues in ToF sensors for applications like robotics or AR/VR, but it is incremental as it adapts an existing diffusion model to a specific domain.
The paper tackles the problem of denoising Time-of-Flight depth data corrupted by severe noise, proposing DepthCAD, which leverages a pretrained Stable Diffusion model with confidence guidance to achieve state-of-the-art performance and robustness in real-world evaluations.
Time-of-Flight (ToF) sensors efficiently capture scene depth, but the nonlinear depth construction procedure often results in extremely large noise variance or even invalid areas. Recent methods based on deep neural networks (DNNs) achieve enhanced ToF denoising accuracy but tend to struggle when presented with severe noise corruption due to limited prior knowledge of ToF data distribution. In this paper, we propose DepthCAD, a novel ToF denoising approach that ensures global structural smoothness by leveraging the rich prior knowledge in Stable Diffusion and maintains local metric accuracy by steering the diffusion process with confidence guidance. To adopt the pretrained image diffusion model to ToF depth denoising, we apply the diffusion on raw ToF correlation measurements with dynamic range normalization before converting to depth maps. Experimental results validate the state-of-the-art performance of the proposed scheme, and the evaluation on real data further verifies its robustness against real-world ToF noise.