SO-NeRF: Active View Planning for NeRF using Surrogate Objectives
This addresses the challenge of efficient data gathering for NeRF, offering a model-agnostic solution that improves view selection for 3D reconstruction.
The paper tackled the problem of actively planning views for Neural Radiance Fields (NeRF) to maximize reconstruction quality, proposing SOAR and SOARNet, which achieved ∼80x speed-up with better or comparable reconstruction results.
Despite the great success of Neural Radiance Fields (NeRF), its data-gathering process remains vague with only a general rule of thumb of sampling as densely as possible. The lack of understanding of what actually constitutes good views for NeRF makes it difficult to actively plan a sequence of views that yield the maximal reconstruction quality. We propose Surrogate Objectives for Active Radiance Fields (SOAR), which is a set of interpretable functions that evaluates the goodness of views using geometric and photometric visual cues - surface coverage, geometric complexity, textural complexity, and ray diversity. Moreover, by learning to infer the SOAR scores from a deep network, SOARNet, we are able to effectively select views in mere seconds instead of hours, without the need for prior visits to all the candidate views or training any radiance field during such planning. Our experiments show SOARNet outperforms the baselines with $\sim$80x speed-up while achieving better or comparable reconstruction qualities. We finally show that SOAR is model-agnostic, thus it generalizes across fully neural-implicit to fully explicit approaches.