发表机构
Tsinghua Shenzhen International Graduate School, Tsinghua University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（清华大学深圳国际研究生院; 广东人工智能与数字经济实验室（深圳））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出联合深度不确定性建模与恢复框架，利用传感器固有不确定性模型和DVSS状态空间网络，解决iToF深度感知中的噪声问题，提升合成与真实数据的恢复精度。
AI 中文摘要
间接飞行时间（iToF）相机能够提供紧凑且成本低廉的稠密深度测量，但在实际成像条件下，其测距精度常因传感器固有不确定性而下降。空间均匀或仅与距离相关的高斯扰动无法准确再现真实iToF测量的距离依赖和信号依赖噪声特性，导致基于学习的恢复方法存在合成到真实的差距。为解决这一问题，我们提出了一种联合深度不确定性建模与恢复框架，以实现可靠的iToF感知。首先，基于标定的抽头响应、返回信号水平和传感器噪声统计，通过面向深度的加权最小二乘公式，开发了传感器固有深度不确定性模型。由此得到的逐像素不确定性用于异方差深度合成和不确定性感知的恢复监督。基于这种异方差数据合成，我们进一步开发了一个带有深度视觉状态空间（DVSS）块的U形恢复网络，该网络将长程状态空间建模与卷积空间-通道细化相结合，以实现保持结构的深度恢复。在合成数据和自研iToF原型机采集的测量数据上的实验，验证了所提出的不确定性模型在不同距离和返回信号条件下的有效性。与固定和距离感知高斯噪声的受控比较，以及对U-Net、Restormer和DVSS的评估，进一步表明所提出的合成方法一致地有益于不同的恢复骨干网络。完整框架在合成测试集上实现了40.85 dB的PSNR和2.54 mm的MAE，在真实iToF测量上实现了35.42 dB的PSNR和4.87 mm的MAE。
英文摘要
Indirect time-of-flight (iToF) cameras provide compact and cost-effective dense depth measurements, but their ranging accuracy is often degraded by sensor-intrinsic uncertainty under practical imaging conditions. Spatially uniform or range-only Gaussian perturbations cannot accurately reproduce the range-dependent and signal-dependent noise characteristics of real iToF measurements, leading to a synthetic-to-real gap for learning-based restoration. To address this problem, we propose a joint depth-uncertainty modeling and restoration framework for reliable iToF sensing. A sensor-intrinsic depth-uncertainty model is first developed from calibrated tap responses, returned-signal levels, and sensor noise statistics through a depth-oriented weighted least-squares formulation. The resulting pixel-wise uncertainty is used for heteroscedastic depth synthesis and uncertainty-aware restoration supervision. Based on this heteroscedastic data synthesis, we further develop a U-shaped restoration network with Depth Visual State Space (DVSS) blocks, which combine long-range state-space modeling with convolutional spatial-channel refinement for structure-preserving depth recovery. Experiments on synthetic data and measurements captured by an in-house iToF prototype validate the proposed uncertainty model under varying range and returned-signal conditions. Controlled comparisons with fixed and range-aware Gaussian noise, together with evaluations on U-Net, Restormer, and DVSS, further demonstrate that the proposed synthesis consistently benefits different restoration backbones. The complete framework achieves 40.85~dB PSNR and 2.54 mm MAE on the synthetic test set, and 35.42 dB PSNR and 4.87 mm MAE on real iToF measurements.
Comments11 pages, 7 figures
