基于RGB-D深度相机的室内场景重建

目的 重建包含真实纹理的彩色场景3维模型是计算机视觉领域重要的研究课题之一,由于室内场景复杂、采样图像序列长且运动无规则,现有的3维重建算法存在重建尺度受限、局部细节重建效果差的等问题。方法 以RGBD-SLAM 算法为基础并提出了两方面的改进,一是将深度图中的平面信息加入帧间配准算法,提高了帧间配准算法的鲁棒性与精度;二是在截断符号距离函数(TSDF)体重建过程中,提出了一种指数权重函数,相比普通的权重函数能更好地减少相机深度畸变对重建的影响。结果 本文方法在相机姿态估计中带来了比RGBD-SLAM方法更好的结果,平均绝对路径误差减少1.3 cm,能取得到更好的重建效果。结论 本文方法有效地提高了相机姿态估计精度,可以应用于室内场景重建中。

Improved RGB-D camera based indoor scene reconstruction

Objective Three-dimensional reconstruction containing texture information is a classical issue in computer vision. Considering the complexity of an indoor scene and the length of sampling image sequence captured from a random moving RGB-D sensor, conventional three-dimensional reconstruction methods suffer from limited scale and perform poor local detail reconstruction effect. Method This paper proposes two improvements of the RGBD-SLAM-based three-dimensional reconstruction algorithm to obtain higher quality reconstruction effect. On the one hand, the plane-primitives are incorporated as constraints to enhance robustness and accuracy of the pair-wise registration algorithm. On the other hand, to reduce the influence of RGB-D sensor large distortion, a novel exponential weight function that is motivated by a Gaussian noise model is proposed. Result In the experiment, the proposed method yields higher quality results compared with state-of-the-art approaches on the benchmarks dataset of the computer vision group of Stanford. Our method also achieves lower average absolute trajectory error compared with a conventional RGB-D SLAM method. Conclusion Experimental results demonstrate that our method substantially increases the accuracy of camera pose estimation and quality of indoor scene three-dimensional reconstruction.