We introduce a novel framework for multiway point cloud mosaicking (named
Wednesday), designed to co-align sets of partially overlapping point clouds --
typically obtained from 3D scanners or moving RGB-D cameras -- into a unified
coordinate system. At the core of our approach is ODIN, a learned pairwise
registration algorithm that iteratively identifies overlaps and refines
attention scores, employing a diffusion-based process for denoising pairwise
correlation matrices to enhance matching accuracy. Further steps include
constructing a pose graph from all point clouds, performing rotation averaging,
a novel robust algorithm for re-estimating translations optimally in terms of
consensus maximization and translation optimization. Finally, the point cloud
rotations and positions are optimized jointly by a diffusion-based approach.
Tested on four diverse, large-scale datasets, our method achieves
state-of-the-art pairwise and multiway registration results by a large margin
on all benchmarks. Our code and models are available at
this https URL

提出了一种新的多通道点云镶嵌框架，其设计用于将部分重叠的点云集合（通常来自 3D 扫描仪或移动 RGB-D 相机）对齐到一个统一的坐标系统；核心算法为 ODIN，它使用扩散过程对相关矩阵进行去噪，并通过迭代识别重叠部分和优化注意力得分来提高匹配精度；进一步的步骤包括构建姿态图，进行旋转平均，使用鲁棒算法重新估计平移以实现共识最大化和优化平移，并最后通过扩散过程联合优化点云的旋转和位置；在四个多样化且大规模的数据集上测试，该方法在所有基准测试中以很大的优势达到了最先进的两两和多通道注册结果。

多通道点云拼接与扩散全局优化

Multiway Point Cloud Mosaicking with Diffusion and Global Optimization

Localization has been a challenging task for autonomous navigation. A loop
detection algorithm must overcome environmental changes for the place
recognition and re-localization of robots. Therefore, deep learning has been
extensively studied for the consistent transformation of measurements into
localization descriptors. Street view images are easily accessible; however,
images are vulnerable to appearance changes. LiDAR can robustly provide precise
structural information. However, constructing a point cloud database is
expensive, and point clouds exist only in limited places. Different from
previous works that train networks to produce shared embedding directly between
the 2D image and 3D point cloud, we transform both data into 2.5D depth images
for matching. In this work, we propose a novel cross-matching method, called
(LC)$^2$, for achieving LiDAR localization without a prior point cloud map. To
this end, LiDAR measurements are expressed in the form of range images before
matching them to reduce the modality discrepancy. Subsequently, the network is
trained to extract localization descriptors from disparity and range images.
Next, the best matches are employed as a loop factor in a pose graph. Using
public datasets that include multiple sessions in significantly different
lighting conditions, we demonstrated that LiDAR-based navigation systems could
be optimized from image databases and vice versa.

本文提出一种名为 (LC)$^2$ 的新型交叉匹配方法，通过将 LiDAR 测量值表达为范围图像进行匹配，然后从视差和范围图像中提取本地化描述符来实现无先前点云地图的 LiDAR 本地化，并在姿势图中将最佳匹配作为循环因素，从而通过公共数据集证明了基于 LiDAR 的导航系统可以从图像数据库中优化，反之亦然。

LC$^2$: 基于 LiDAR 和相机循环约束的跨模态地点识别

(LC)$^2$: LiDAR-Camera Loop Constraints For Cross-Modal Place  Recognition

In this paper, we present a new method for the multiview registration of
point cloud. Previous multiview registration methods rely on exhaustive
pairwise registration to construct a densely-connected pose graph and apply
Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the
scan poses. However, constructing a densely-connected graph is time-consuming
and contains lots of outlier edges, which makes the subsequent IRLS struggle to
find correct poses. To address the above problems, we first propose to use a
neural network to estimate the overlap between scan pairs, which enables us to
construct a sparse but reliable pose graph. Then, we design a novel history
reweighting function in the IRLS scheme, which has strong robustness to outlier
edges on the graph. In comparison with existing multiview registration methods,
our method achieves 11% higher registration recall on the 3DMatch dataset and
~13% lower registration errors on the ScanNet dataset while reducing ~70%
required pairwise registrations. Comprehensive ablation studies are conducted
to demonstrate the effectiveness of our designs.

本文提出了一种新的多视点点云配准方法，使用神经网络评估扫描对之间的重叠，构建稀疏的姿态图，并在 IRLS 方案中设计了一种新的历史加权函数以提高对异常边缘的强鲁棒性，实验结果表明，相对于现有方法，我们的方法在 3DMatch 数据集上实现了 11% 的更高配准召回率，在 ScanNet 数据集上实现了约 13% 的更低配准误差，降低了约 70% 的必要的成对注册。