在无纹理黑暗环境中，用投影图案实现三维重建与设备位姿同步估计。
Neural Active Structure-from-Motion in Dark and Textureless Environment
- 基于神经符号距离场，从稀疏投影图案中联合优化场景形状与系统位姿。
- 在无纹理、低光照条件下仍可实现高精度三维重建与位姿估计。
- 适用于移动式结构光系统，适合机器人导航与工业检测场景。
主动3D测量，尤其是结构光（SL），因其对无纹理或弱光照表面的鲁棒性，在多个领域广泛应用。此外，通过移动结构光系统来重建大场景也日益流行，但大多数传统技术依赖图像特征进行位姿估计，而这些特征在无纹理环境下难以提取。本文提出一种从仅观察到稀疏投影图案的图像集合中，同时实现结构光系统下场景形状重建与位姿估计的新方法，称为主动SfM（Active SfM）。为此，我们设计了一个完整的优化框架，采用神经符号距离场（Neural-SDF）表示体积化场景形状，目标不仅是重建场景几何，还包括估计系统在每次运动中的精确位姿。实验结果表明，该方法可在仅含投影图案、无场景纹理信息的图像中实现准确的三维重建与位姿估计。
原文摘要 · Abstract (English)
Active 3D measurement, especially structured light (SL) has been widely used in various fields for its robustness against textureless or equivalent surfaces by low light illumination. In addition, reconstruction of large scenes by moving the SL system has become popular, however, there have been few practical techniques to obtain the system's precise pose information only from images, since most conventional techniques are based on image features, which cannot be retrieved under textureless environments. In this paper, we propose a simultaneous shape reconstruction and pose estimation technique for SL systems from an image set where sparsely projected patterns onto the scene are observed (i.e. no scene texture information), which we call Active SfM. To achieve this, we propose a full optimization framework of the volumetric shape that employs neural signed distance fields (Neural-SDF) for SL with the goal of not only reconstructing the scene shape but also estimating the poses for each motion of the system. Experimental results show that the proposed method is able to achieve accurate shape reconstruction as well as pose estimation from images where only projected patterns are observed.
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