用神经距离场提升弱光/散射环境下的立体重建精度
ActiveNeuS: Neural Signed Distance Fields for Active Stereo
- 用神经符号距离场实现通用结构光下的隐式匹配与三角测量
- 仅需少量图像即可重建无纹理或低光照表面，效果优于现有方法
- 适用于水下等极端场景，对设备要求更低
在低光照或散射等极端环境下进行3D形状重建仍是开放难题，主动立体视觉因其鲁棒性和高精度成为潜在解决方案。然而，现有主动立体系统多依赖专用配置和复杂算法，应用受限。本文提出面向主动立体系统的神经符号距离场（Neural Signed Distance Field），实现通用结构光下的隐式对应关系搜索与三角测量。该方法可在仅采集少量图像的情况下，成功重建纹理缺失或受低光照影响的表面。实验表明，在严苛条件下本方法能达到当前最优的重建质量，并在水下场景中验证了有效性。
原文摘要 · Abstract (English)
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.
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