  There is an emerging effort to combine the two popular 3D frameworks using
Multi-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specific
focus on the few-shot / sparse view setting. In this paper, we introduce a
novel integration scheme that combines the multi-view stereo with neural signed
distance function representations, which potentially overcomes the limitations
of both methods. MVS uses per-view depth estimation and cross-view fusion to
generate accurate surfaces, while NIS relies on a common coordinate volume.
Based on this strategy, we propose to construct per-view cost frustum for finer
geometry estimation, and then fuse cross-view frustums and estimate the
implicit signed distance functions to tackle artifacts that are due to noise
and holes in the produced surface reconstruction. We further apply a cascade
frustum fusion strategy to effectively captures global-local information and
structural consistency. Finally, we apply cascade sampling and a
pseudo-geometric loss to foster stronger integration between the two
architectures. Extensive experiments demonstrate that our method reconstructs
robust surfaces and outperforms existing state-of-the-art methods.
