Albedo estimation for real-time 3D reconstruction using RGB-D and IR data
Albedo estimation for real-time 3D reconstruction using RGB-D and IR data
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DOI：
10.1016/j.isprsjprs.2019.01.018
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发表时间：
2019-04
影响因子：
12.7
通讯作者：
P. Stotko;Michael Weinmann;R. Klein
中科院分区：
文献类型：
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作者：
P. Stotko;Michael Weinmann;R. Klein
利用低成本传感器实时重建场景在最近的研究中得到了越来越多的关注，并使其在图形、视觉和机器人领域得到了广泛的应用。虽然目前的技术提供了关于重建几何的质量的实质性改进，但由于表面几何、反射特性和周围照明的复杂相互作用，重建准确的表面外观是非常具有挑战性的，因此总体外观的逼真程度仍然不足。我们提出了一种新的方法，允许从通过商品传感器获得的RGB-D和IR数据重建场景的几何和空间变化的表面反照率。与以前的方法相比，我们的方法提供了更好的稳健性和显著的加速，甚至可以满足实时要求。为此，我们利用场景分割的优点来改进反照率估计，这是由于IR和RGB数据的分段耦合更好地考虑了场景中不同材料的波长特征。使用一种新的加权方案将估计的反照率直接纳入密集体积重建框架，以产生高质量的结果。在我们的评估中，我们证明了我们的方法允许捕获复杂场景的反照率，包括复杂、高频和强烈变化的光照和阴影。
Reconstructing scenes in real-time using low-cost sensors has gained increasing attention in recent research and enabled numerous applications in graphics, vision, and robotics. While current techniques offer a substantial improvement regarding the quality of the reconstructed geometry, the degree of realism of the overall appearance is still lacking as the reconstruction of accurate surface appearance is highly challenging due to the complex interplay of surface geometry, reflectance properties and surrounding illumination. We present a novel approach that allows the reconstruction of both the geometry and the spatially varying surface albedo of a scene from RGB-D and IR data obtained via commodity sensors. In comparison to previous approaches, our approach offers an improved robustness and a significant speed-up to even fulfill the real-time requirements. For this purpose, we exploit the benefits of scene segmentation to improve albedo estimation due to the resulting better segment-wise coupling of IR and RGB data that takes into account the wavelength characteristics of different materials within the scene. The estimated albedo is directly integrated into the dense volumetric reconstruction framework using a novel weighting scheme to generate high-quality results. In our evaluation, we demonstrate that our approach allows albedo capturing of complicated scenarios including complex, high-frequent and strongly varying lighting as well as shadows.