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

The extension of convolutional neural networks (CNNs) to non-Euclidean
geometries has led to multiple frameworks for studying manifolds. Many of those
methods have shown design limitations resulting in poor modelling of long-range
associations, as the generalisation of convolutions to irregular surfaces is
non-trivial. Recent state-of-the-art performance of Vision Transformers (ViTs)
demonstrates that a general-purpose architecture, which implements
self-attention, could replace the local feature learning operations of CNNs.
Motivated by the success of attention-modelling in computer vision, we extend
ViTs to surfaces by reformulating the task of surface learning as a
sequence-to-sequence problem and propose a patching mechanism for surface
meshes. We validate the performance of the proposed Surface Vision Transformer
(SiT) on two brain age prediction tasks in the developing Human Connectome
Project (dHCP) dataset and investigate the impact of pre-training on model
performance. Experiments show that the SiT outperforms many surface CNNs, while
indicating some evidence of general transformation invariance. Code available
at this https URL

该论文介绍了将自注意力模型应用于通过序列到序列问题学习曲面表示，并提出一种可应用于不规则曲面网格的表面视觉 Transformer（SiT）模型，该模型提高了两项大脑年龄预测任务的精度。

使用视觉 Transformer 进行表面分析

Surface Analysis with Vision Transformers

We present a convolutional network capable of inferring a 3D representation
of a previously unseen object given a single image of this object. Concretely,
the network can predict an RGB image and a depth map of the object as seen from
an arbitrary view. Several of these depth maps fused together give a full point
cloud of the object. The point cloud can in turn be transformed into a surface
mesh. The network is trained on renderings of synthetic 3D models of cars and
chairs. It successfully deals with objects on cluttered background and
generates reasonable predictions for real images of cars.

本文提出了一种卷积网络，可以仅仅基于单张图像预测出物体的 RGB 图和深度图，并将多幅深度图融合成完整点云后可转换成表面网格来对任意视野下的三维物体进行识别。网络通过合成的三维车和椅子模型进行训练，在有杂乱背景并搭配真实车辆图像的情况下，仍可生成合理的推测。