ActiveNeRF: Learning Accurate 3D Geometry by Active Pattern Projection
This addresses the geometry accuracy issue in 3D reconstruction for computer vision applications, representing an incremental improvement over existing NeRF methods.
The paper tackles the problem of inaccurate implicit geometry in Neural Radiance Fields (NeRFs) due to low spatial frequency in passive illumination, and proposes ActiveNeRF, which actively projects high-frequency patterns to improve geometry reconstruction, achieving state-of-the-art results in simulation and real experiments with quantitative gains.
NeRFs have achieved incredible success in novel view synthesis. However, the accuracy of the implicit geometry is unsatisfactory because the passive static environmental illumination has low spatial frequency and cannot provide enough information for accurate geometry reconstruction. In this work, we propose ActiveNeRF, a 3D geometry reconstruction framework, which improves the geometry quality of NeRF by actively projecting patterns of high spatial frequency onto the scene using a projector which has a constant relative pose to the camera. We design a learnable active pattern rendering pipeline which jointly learns the scene geometry and the active pattern. We find that, by adding the active pattern and imposing its consistency across different views, our proposed method outperforms state of the art geometry reconstruction methods qualitatively and quantitatively in both simulation and real experiments. Code is avaliable at https://github.com/hcp16/active_nerf