Generating wide-area digital surface models (DSMs) requires registering a
large number of individual, and partially overlapped DSMs. This presents a
challenging problem for a typical registration algorithm, since when a large
number of observations from these multiple DSMs are considered, it may easily
cause memory overflow. Sequential registration algorithms, although can
significantly reduce the computation, are especially vulnerable for small
overlapped pairs, leading to a large error accumulation. In this work, we
propose a novel solution that builds the DSM registration task as a motion
averaging problem: pair-wise DSMs are registered to build a scene graph, with
edges representing relative poses between DSMs. Specifically, based on the grid
structure of the large DSM, the pair-wise registration is performed using a
novel nearest neighbor search method. We show that the scene graph can be
optimized via an extremely fast motion average algorithm with O(N) complexity
(N refers to the number of images). Evaluation of high-resolution
satellite-derived DSM demonstrates significant improvement in computation and
accuracy.

通过将数字表面模型注册任务建立为一个运动平均问题，我们提出了一种新的解决方案：成对的数字表面模型通过建立场景图来注册，其中边表示数字表面模型之间的相对姿势。根据大型数字表面模型的网格结构，采用一种新的最近邻搜索方法进行成对的注册。通过极快的运动平均算法对场景图进行优化，实验证明在计算和准确性方面有显著的改进。

大规模 DSM 注册通过动作平均化

Large-scale DSM registration via motion averaging

Multi-instance point cloud registration estimates the poses of multiple
instances of a model point cloud in a scene point cloud. Extracting accurate
point correspondence is to the center of the problem. Existing approaches
usually treat the scene point cloud as a whole, overlooking the separation of
instances. Therefore, point features could be easily polluted by other points
from the background or different instances, leading to inaccurate
correspondences oblivious to separate instances, especially in cluttered
scenes. In this work, we propose MIRETR, Multi-Instance REgistration
TRansformer, a coarse-to-fine approach to the extraction of instance-aware
correspondences. At the coarse level, it jointly learns instance-aware
superpoint features and predicts per-instance masks. With instance masks, the
influence from outside of the instance being concerned is minimized, such that
highly reliable superpoint correspondences can be extracted. The superpoint
correspondences are then extended to instance candidates at the fine level
according to the instance masks. At last, an efficient candidate selection and
refinement algorithm is devised to obtain the final registrations. Extensive
experiments on three public benchmarks demonstrate the efficacy of our
approach. In particular, MIRETR outperforms the state of the arts by 16.6
points on F1 score on the challenging ROBI benchmark. Code and models are
available at this https URL

通过 MIRETR，即 Multi-Instance REgistration TRansformer 方法，我们提出了一种粗到细的方法来提取具有实例感知的对应关系，通过联合学习实例感知超点特征和预测每个实例的掩码，从而最小化关注范围外的影响，提取可靠的超点对应关系，并根据实例掩码将超点对应关系扩展到实例候选项，最后通过高效的候选项选择和精调算法获取最终的配准结果。在三个公共基准测试中进行了广泛实验，结果表明我们的方法的有效性。特别是在具有挑战性的 ROBI 基准测试中，MIRETR 在 F1 得分上超过了现有技术 16.6 个点。

学习鲁棒的多实例点云配准中的实例感知对应关系

Learning Instance-Aware Correspondences for Robust Multi-Instance Point  Cloud Registration in Cluttered Scenes

We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
this https URL

本文介绍了一种新颖的端到端可学习的多视角三维点云拼接算法，该算法集成了两阶段流程（成对对准和全局优化），并且是当前领先技术的改进。