论文标题
带有结构光的多视图神经表面重建
Multi-View Neural Surface Reconstruction with Structured Light
论文作者
论文摘要
基于可区分渲染（DR）的三维（3D）对象重建是计算机视觉中的一个积极研究主题。基于DR的方法通过优化形状和外观并实现高视觉生殖率来最大程度地减少渲染图像和目标图像之间的差异。但是，由于几何模棱两可，大多数方法对无纹理对象的表现较差，这意味着多种形状可以在此类对象中具有相同的渲染结果。为了克服这个问题，我们将带有结构性光（SL）的主动传感引入基于DR的多视图3D对象重建中，以了解任意场景和相机姿势的未知几何形状和外观。更具体地说，我们的框架利用由结构光计算的不同视图中像素之间的对应关系，作为基于DR的隐式表面，颜色表示和相机姿势的基于DR的优化的附加约束。由于可以同时优化相机姿势，因此我们的方法实现了无纹理区域的高重建精度，并减少了相机姿势校准的努力，这对于传统的基于SL的方法是必需的。合成数据和实际数据的实验结果表明，我们的系统在高质量的表面重建中优于基于DR的常规方法，尤其是对于具有无纹理或光泽表面的具有挑战性的对象。
Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approaches perform poorly for textureless objects because of the geometrical ambiguity, which means that multiple shapes can have the same rendered result in such objects. To overcome this problem, we introduce active sensing with structured light (SL) into multi-view 3D object reconstruction based on DR to learn the unknown geometry and appearance of arbitrary scenes and camera poses. More specifically, our framework leverages the correspondences between pixels in different views calculated by structured light as an additional constraint in the DR-based optimization of implicit surface, color representations, and camera poses. Because camera poses can be optimized simultaneously, our method realizes high reconstruction accuracy in the textureless region and reduces efforts for camera pose calibration, which is required for conventional SL-based methods. Experiment results on both synthetic and real data demonstrate that our system outperforms conventional DR- and SL-based methods in a high-quality surface reconstruction, particularly for challenging objects with textureless or shiny surfaces.
