In this paper, we investigate a new optimization framework for multi-view 3D shape reconstructions. Recent differentiable rendering approaches have provided breakthrough performances with implicit shape representations though they can still lack precision in the estimated geometries. On the other hand multi-view stereo methods can yield pixel wise geometric accuracy with local depth predictions along viewing rays. Our approach bridges the gap between the two strategies with a novel volumetric shape representation that is implicit but parameterized with pixel depths to better materialize the shape surface with consistent signed distances along viewing rays. The approach retains pixel-accuracy while benefiting from volumetric integration in the optimization. To this aim, depths are optimized by evaluating, at each 3D location within the volumetric discretization, the agreement between the depth prediction consistency and the photometric consistency for the corresponding pixels. The optimization is agnostic to the associated photo-consistency term which can vary from a median-based baseline to more elaborate criteria learned functions. Our experiments demonstrate the benefit of the volumetric integration with depth predictions. They also show that our approach outperforms existing approaches over standard 3D benchmarks with better geometry estimations.

翻译：本文研究了一种用于多视角三维形状重建的新优化框架。最近的微分渲染方法利用隐式形状表示取得了突破性进展，但所估计的几何体仍可能缺乏精度；另一方面，多视角立体方法可通过沿观察射线的局部深度预测实现像素级几何精度。我们的方法通过一种新颖的体积形状表示弥合了这两种策略的差距——该表示是隐式的，但通过像素深度参数化，以沿观察射线保持一致的符号距离更好地体现形状表面。该方法在保持像素级精度的同时，受益于优化过程中的体积集成。为此，深度值通过评估体积离散化中每个三维位置的深度预测一致性及对应像素的光度一致性进行优化。该优化与光度一致性项无关，后者可从基于中位数的基线方法到更复杂的基于学习准则的函数。实验证明了体积集成与深度预测相结合的优势，并表明我们的方法在标准三维基准测试中因更优的几何估计而优于现有方法。