Recent works have explored the fundamental role of depth estimation in multi-view stereo (MVS) and semantic scene completion (SSC). They generally construct 3D cost volumes to explore geometric correspondence in depth, and estimate such volumes in a single step relying directly on the ground truth approximation. However, such problem cannot be thoroughly handled in one step due to complex empirical distributions, especially in challenging regions like occlusions, reflections, etc. In this paper, we formulate the depth estimation task as a multi-step distribution approximation process, and introduce a new paradigm of modeling the Volumetric Probability Distribution progressively (step-by-step) following a Markov chain with Diffusion models (VPDD). Specifically, to constrain the multi-step generation of volume in VPDD, we construct a meta volume guidance and a confidence-aware contextual guidance as conditional geometry priors to facilitate the distribution approximation. For the sampling process, we further investigate an online filtering strategy to maintain consistency in volume representations for stable training. Experiments demonstrate that our plug-and-play VPDD outperforms the state-of-the-arts for tasks of MVS and SSC, and can also be easily extended to different baselines to get improvement. It is worth mentioning that we are the first camera-based work that surpasses LiDAR-based methods on the SemanticKITTI dataset.

翻译：近期研究已深入探索深度估计在多视角立体（MVS）与语义场景补全（SSC）中的核心作用。此类方法通常构建三维代价体以挖掘深度信息中的几何对应关系，并直接基于真实值近似实现单步估计。然而，由于存在复杂的经验分布，尤其在遮挡、反射等挑战性区域，此类问题难以通过单步方式充分求解。本文创新性地将深度估计任务建模为多步分布逼近过程，提出基于扩散模型沿马尔可夫链渐进（逐步骤）建模体素概率分布（VPDD）的新范式。具体而言，为约束VPDD中体素的多步生成，我们构建元体素引导与置信度感知上下文引导作为条件几何先验，以促进分布逼近。在采样过程中，我们进一步提出在线滤波策略以维持体素表征一致性，确保训练稳定性。实验表明，所提即插即用型VPDD在MVS与SSC任务中均超越当前最优方法，且可便捷扩展至不同基线以提升性能。值得一提的是，这是首个在SemanticKITTI数据集上超越激光雷达方法的纯相机方案。