Learning-based stereo matching techniques have made significant progress.
However, existing methods inevitably lose geometrical structure information
during the feature channel generation process, resulting in edge detail
mismatches. In this paper, the Motif Cha}nnel Attention Stereo Matching Network
(MoCha-Stereo) is designed to address this problem. We provide the Motif
Channel Correlation Volume (MCCV) to determine more accurate edge matching
costs. MCCV is achieved by projecting motif channels, which capture common
geometric structures in feature channels, onto feature maps and cost volumes.
In addition, edge variations in %potential feature channels of the
reconstruction error map also affect details matching, we propose the
Reconstruction Error Motif Penalty (REMP) module to further refine the
full-resolution disparity estimation. REMP integrates the frequency information
of typical channel features from the reconstruction error. MoCha-Stereo ranks
1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure
also shows excellent performance in Multi-View Stereo. Code is avaliable at
this https URL

通过引入 Motif Channel Attention Stereo Matching Network (MoCha-Stereo) 解决现有方法中在特征通道生成过程中丢失几何结构信息的问题，并提出 Motif Channel Correlation Volume (MCCV) 和 Reconstruction Error Motif Penalty (REMP) 模块来提高边缘匹配成本和细节匹配的估计性能。

MoCha-Stereo：用于立体匹配的模样通道注意力网络

MoCha-Stereo: Motif Channel Attention Network for Stereo Matching

With the overwhelming trend of mask image modeling led by MAE, generative
pre-training has shown a remarkable potential to boost the performance of
fundamental models in 2D vision. However, in 3D vision, the over-reliance on
Transformer-based backbones and the unordered nature of point clouds have
restricted the further development of generative pre-training. In this paper,
we propose a novel 3D-to-2D generative pre-training method that is adaptable to
any point cloud model. We propose to generate view images from different
instructed poses via the cross-attention mechanism as the pre-training scheme.
Generating view images has more precise supervision than its point cloud
counterpart, thus assisting 3D backbones to have a finer comprehension of the
geometrical structure and stereoscopic relations of the point cloud.
Experimental results have proved the superiority of our proposed 3D-to-2D
generative pre-training over previous pre-training methods. Our method is also
effective in boosting the performance of architecture-oriented approaches,
achieving state-of-the-art performance when fine-tuning on ScanObjectNN
classification and ShapeNetPart segmentation tasks. Code is available at
this https URL

我们提出了一种新颖的适用于任何点云模型的 3D 到 2D 生成预训练方法，通过交叉注意机制生成来自不同指示姿势的视图图像作为预训练方案，以精确监督帮助 3D 主干更好地理解点云的几何结构和立体关系，实验结果证明了我们提出的方法在提升架构导向方法性能方面的优越性，并在 Fine-tuning ScanObjectNN 分类和 ShapeNetPart 分割任务时实现了最先进的性能。

拍照：点云模型的 3D 到 2D 生成预训练

Take-A-Photo: 3D-to-2D Generative Pre-training of Point Cloud Models

Cellular sheaves equip graphs with a "geometrical" structure by assigning
vector spaces and linear maps to nodes and edges. Graph Neural Networks (GNNs)
implicitly assume a graph with a trivial underlying sheaf. This choice is
reflected in the structure of the graph Laplacian operator, the properties of
the associated diffusion equation, and the characteristics of the convolutional
models that discretise this equation. In this paper, we use cellular sheaf
theory to show that the underlying geometry of the graph is deeply linked with
the performance of GNNs in heterophilic settings and their oversmoothing
behaviour. By considering a hierarchy of increasingly general sheaves, we study
how the ability of the sheaf diffusion process to achieve linear separation of
the classes in the infinite time limit expands. At the same time, we prove that
when the sheaf is non-trivial, discretised parametric diffusion processes have
greater control than GNNs over their asymptotic behaviour. On the practical
side, we study how sheaves can be learned from data. The resulting sheaf
diffusion models have many desirable properties that address the limitations of
classical graph diffusion equations (and corresponding GNN models) and obtain
competitive results in heterophilic settings. Overall, our work provides new
connections between GNNs and algebraic topology and would be of interest to
both fields.

本文探讨细胞鞘理论在图神经网络研究中的应用，研究表明图的基础几何结构与 GNN 在异质性场景下的过度平滑行为有深刻关联。通过学习来自数据的 sheaf，可以获得具有竞争力的表现。