MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereo

We present MVSNeRF, a novel neural rendering approach that can efficiently reconstruct neural radiance fields for view synthesis. Our approach can generalize across scenes (even indoor scenes, completely different from our training scenes of objects) and generate realistic view synthesis results using only three input images, significantly outperforming concurrent works on generalizable radiance field reconstruction.

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• 1 Unlike prior works on neural radiance fields that consider per-scene optimization on densely captured images, we propose a generic deep neural network that can reconstruct radiance fields from only three nearby input views via fast network inference.
• 2 Our approach leverages plane-swept cost volumes (widely used in multi-view stereo) for geometry-aware scene reasoning, and combines this with physically based volume rendering for neural radiance field reconstruction.
• 3 We train our network on real objects in the DTU dataset, and test it on three different datasets to evaluate its effectiveness and generalizability.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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