Recovering an outdoor environment's surface mesh is vital for an agricultural
robot during task planning and remote visualization. Our proposed solution is
based on a newly-designed panoramic stereo camera along with a hybrid novel
software framework that consists of three fusion modules. The panoramic stereo
camera with a pentagon shape consists of 5 stereo vision camera pairs to stream
synchronized panoramic stereo images for the following three fusion modules. In
the disparity fusion module, rectified stereo images produce the initial
disparity maps using multiple stereo vision algorithms. Then, these initial
disparity maps, along with the intensity images, are input into a disparity
fusion network to produce refined disparity maps. Next, the refined disparity
maps are converted into full-view point clouds or single-view point clouds for
the pose fusion module. The pose fusion module adopts a two-stage
global-coarse-to-local-fine strategy. In the first stage, each pair of
full-view point clouds is registered by a global point cloud matching algorithm
to estimate the transformation for a global pose graph's edge, which
effectively implements loop closure. In the second stage, a local point cloud
matching algorithm is used to match single-view point clouds in different
nodes. Next, we locally refine the poses of all corresponding edges in the
global pose graph using three proposed rules, thus constructing a refined pose
graph. The refined pose graph is optimized to produce a global pose trajectory
for volumetric fusion. In the volumetric fusion module, the global poses of all
the nodes are used to integrate the single-view point clouds into the volume to
produce the mesh of the whole garden. The proposed framework and its three
fusion modules are tested on a real outdoor garden dataset to show the
superiority of the performance.

提出了一个基于新设计全景立体相机和混合软件框架的解决方案，具有三个融合模块，包括视差融合、姿态融合和立体体积融合，用于从单视图点云中构建整个花园的网格。

一种基于全景立体图像的多模态园艺数据集和混合三维密集重建框架，用于修剪机器人

A Multi-modal Garden Dataset and Hybrid 3D Dense Reconstruction  Framework Based on Panoramic Stereo Images for a Trimming Robot

In this work, we present a dense tracking and mapping system named
Vox-Fusion, which seamlessly fuses neural implicit representations with
traditional volumetric fusion methods. Our approach is inspired by the recently
developed implicit mapping and positioning system and further extends the idea
so that it can be freely applied to practical scenarios. Specifically, we
leverage a voxel-based neural implicit surface representation to encode and
optimize the scene inside each voxel. Furthermore, we adopt an octree-based
structure to divide the scene and support dynamic expansion, enabling our
system to track and map arbitrary scenes without knowing the environment like
in previous works. Moreover, we proposed a high-performance multi-process
framework to speed up the method, thus supporting some applications that
require real-time performance. The evaluation results show that our methods can
achieve better accuracy and completeness than previous methods. We also show
that our Vox-Fusion can be used in augmented reality and virtual reality
applications. Our source code is publicly available at
this https URL

本文提出了一种名为 Vox-Fusion 的密集跟踪和建图系统，其将神经隐式表示与传统体积融合方法无缝融合。该方法通过采用基于体素的神经隐式表面表示来编码和优化每个体素内部的场景，并采用基于八叉树的结构来划分场景和支持动态扩展，进而实现对任意场景的跟踪和建图。此外，我们提出了一个高性能的多进程框架来加速该方法，从而支持某些需要实时性能的应用。评估结果表明，我们的方法可以实现比先前方法更好的准确性和完整性。我们还展示了我们的 Vox-Fusion 可用于增强现实和虚拟现实应用。