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There is an emerging effort to combine the two popular 3D frameworks usingMulti-View Stereo (MVS) and Neural Implicit Surfaces (NIS) with a specificfocus on the few-shot / sparse view setting. In this paper, we introduce anovel integration scheme that combines the multi-view stereo with neural signeddistance function representations, which potentially overcomes the limitationsof both methods. MVS uses per-view depth estimation and cross-view fusion togenerate accurate surfaces, while NIS relies on a common coordinate volume.Based on this strategy, we propose to construct per-view cost frustum for finergeometry estimation, and then fuse cross-view frustums and estimate theimplicit signed distance functions to tackle artifacts that are due to noiseand holes in the produced surface reconstruction. We further apply a cascadefrustum fusion strategy to effectively captures global-local information andstructural consistency. Finally, we apply cascade sampling and apseudo-geometric loss to foster stronger integration between the twoarchitectures. Extensive experiments demonstrate that our method reconstructsrobust surfaces and outperforms existing state-of-the-art methods.

C2F2NeUS: Cascade Cost Frustum Fusion for High Fidelity and Generalizable Neural Surface Reconstruction