  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.
