ActiveNeuS: Neural Signed Distance Fields for Active Stereo
This addresses the problem of robust 3D reconstruction in challenging conditions for applications like underwater imaging, though it appears incremental as it builds on existing active stereo and neural field methods.
The paper tackles 3D shape reconstruction in extreme environments like low illumination by proposing Neural Signed Distance Fields for active stereo systems, achieving state-of-the-art reconstruction quality with a small number of images, as demonstrated in underwater scenarios.
3D-shape reconstruction in extreme environments, such as low illumination or scattering condition, has been an open problem and intensively researched. Active stereo is one of potential solution for such environments for its robustness and high accuracy. However, active stereo systems usually consist of specialized system configurations with complicated algorithms, which narrow their application. In this paper, we propose Neural Signed Distance Field for active stereo systems to enable implicit correspondence search and triangulation in generalized Structured Light. With our technique, textureless or equivalent surfaces by low light condition are successfully reconstructed even with a small number of captured images. Experiments were conducted to confirm that the proposed method could achieve state-of-the-art reconstruction quality under such severe condition. We also demonstrated that the proposed method worked in an underwater scenario.