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Neural Radiance Fields, or NeRFs, have drastically improved novel viewsynthesis and 3D reconstruction for rendering. NeRFs achieve impressive resultson object-centric reconstructions, but the quality of novel view synthesis withfree-viewpoint navigation in complex environments (rooms, houses, etc) is oftenproblematic. While algorithmic improvements play an important role in theresulting quality of novel view synthesis, in this work, we show that becauseoptimizing a NeRF is inherently a data-driven process, good quality data play afundamental role in the final quality of the reconstruction. As a consequence,it is critical to choose the data samples -- in this case the cameras -- in away that will eventually allow the optimization to converge to a solution thatallows free-viewpoint navigation with good quality. Our main contribution is analgorithm that efficiently proposes new camera placements that improve visualquality with minimal assumptions. Our solution can be used with any NeRF modeland outperforms baselines and similar work.

Improving NeRF Quality by Progressive Camera Placement for Unrestricted Navigation in Complex Environments