On Bundle Adjustment for Multiview Point Cloud Registration
Huaiyang Huang, Yuxiang Sun, Jin Wu, Jianhao Jiao, Xiangcheng Hu, Linwei Zheng, Lujia Wang, Ming Liu
Abstract
Multiview registration is used to estimate Rigid Body Transformations (RBTs) from multiple frames and reconstruct a scene with corresponding scans. Despite the success of pairwise registration and pose synchronization, the concept of Bundle Adjustment (BA) has been proven to better maintain global consistency. So in this work, we make the multiview point-cloud registration more tractable from a different perspective in resolving range-based BA. We first analyse the optimal condition of the objective function of BA that unifies some previous approaches. Based on this analysis, we propose an objective function that takes both measurement noises and computational cost into account. For the feature parameter update, instead of calculating the global distribution parameters from the raw measurements, we aggregate the local distributions in a frame-wise fashion at each iteration. The computational cost of feature update is then only dependent on the number of scans. Finally, we develop a multiview registration system using voxel-based quantization that can be applied in real-world scenarios. The experimental results demonstrate our superiority over the baselines in terms of both accuracy and speed. Moreover, the results also show that our average positioning errors achieve the centimeter level. Related materials are available at our project page https://hyhuang1995.github.io/bareg/.