使用秩-1的小运动深度
Depth from Small Motion using Rank-1 Initialization
摘要：
小运动深度（DfSM）（Ha等人，2016）对于商业手持设备特别有趣，因为它允许以最小的用户努力和合作获得深度信息的可能性。由于这些设备的速度和内存问题，使用光束平差（BA）的方法的自校准优化需要少至10-15张图像。因此，优化往往需要多次迭代才能收敛，或者在某些情况下可能根本不收敛。这项工作提出了一个稳健的初始化光束法平差使用秩1分解方法（Tomasi和Kanade，1992年），（Aguiar和Albera，1999年a）。我们创建一个约束矩阵，在无噪声的情况下，秩为1，然后使用SVD来计算逆深度值和相机运动。我们只需要大约四分之一的光束法平差迭代就可以收敛。我们还提出了网格化的特征提取技术，使只有重要的和小的功能被跟踪的图像帧。这也确保了在移动终端上的全部执行时间中的加速。对于实验，我们已经记录了在两个移动终端平台上使用CPU-GPU协同处理的优化加速的建议的Rank-1初始化的执行时间。秩1-BA的组合生成更鲁棒的深度图，并且比单独使用BA显著更快。
英文摘要：
Depth from Small Motion (DfSM) (Ha et al., 2016) is particularly interesting for commercial handheld devices because it allows the possibility to get depth information with minimal user effort and cooperation. Due to speed and memory issue on these devices, the self calibration optimization of the method using Bundle Adjustment (BA) need as little as 10-15 images. Therefore, the optimization tends to take many iterations to converge or may not converge at all in some cases. This work propose a robust initialization for the bundle adjustment using the rank-1 factorization method (Tomasi and Kanade, 1992), (Aguiar and Moura, 1999a). We create a constraint matrix that is rank-1 in a noiseless situation, then use SVD to compute the inverse depth values and the camera motion. We only need about quarter fraction of the bundle adjustment iteration to converge. We also propose grided feature extraction technique so that only important and small features are tracked all over the image frames. This also ensure speedup in the full execution time on the mobile device. For the experiments, we have documented the execution time with the proposed Rank-1 initialization on two mobile device platforms using optimized accelerations with CPU-GPU co-processing. The combination of Rank 1-BA generates more robust depth-map and is significantly faster than using BA alone.
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