IEEE Trans Vis Comput Graph. 2018 Feb;24(2):1190-1203. doi: 10.1109/TVCG.2017.2657766. Epub 2017 Jan 25.
Handheld scanning using commodity depth cameras provides a flexible and low-cost manner to get 3D models. The existing methods scan a target by densely fusing all the captured depth images, yet most frames are redundant. The jittering frames inevitably embedded in handheld scanning process will cause feature blurring on the reconstructed model and even trigger the scan failure (i.e., camera tracking losing). To address these problems, in this paper, we propose a novel sparse-sequence fusion (SSF) algorithm for handheld scanning using commodity depth cameras. It first extracts related measurements for analyzing camera motion. Then based on these measurements, we progressively construct a supporting subset for the captured depth image sequence to decrease the data redundancy and the interference from jittering frames. Since SSF will reveal the intrinsic heavy noise of the original depth images, our method introduces a refinement process to eliminate the raw noise and recover geometric features for the depth images selected into the supporting subset. We finally obtain the fused result by integrating the refined depth images into the truncated signed distance field (TSDF) of the target. Multiple comparison experiments are conducted and the results verify the feasibility and validity of SSF for handheld scanning with a commodity depth camera.
使用商品深度相机进行手持式扫描提供了一种灵活且低成本的方式来获取 3D 模型。现有的方法通过密集融合所有捕获的深度图像来扫描目标，但大多数帧都是冗余的。手持式扫描过程中不可避免的抖动帧会导致重建模型上的特征模糊，甚至触发扫描失败（即相机跟踪丢失）。为了解决这些问题，本文提出了一种用于商品深度相机的手持式扫描的新型稀疏序列融合（SSF）算法。它首先提取相关测量值来分析相机运动。然后基于这些测量值，我们逐步为捕获的深度图像序列构建一个支持子集，以减少数据冗余和抖动帧的干扰。由于 SSF 会揭示原始深度图像的固有强噪声，因此我们的方法引入了一种细化过程，以消除原始噪声并恢复支持子集中选择的深度图像的几何特征。我们最后通过将细化后的深度图像集成到目标的截断符号距离场（TSDF）中，得到融合结果。进行了多项对比实验，结果验证了 SSF 用于商品深度相机的手持式扫描的可行性和有效性。
