Mesh denoising, aimed at removing noise from input meshes while preserving
their feature structures, is a practical yet challenging task. Despite the
remarkable progress in learning-based mesh denoising methodologies in recent
years, their network designs often encounter two principal drawbacks: a
dependence on single-modal geometric representations, which fall short in
capturing the multifaceted attributes of meshes, and a lack of effective global
feature aggregation, hindering their ability to fully understand the mesh's
comprehensive structure. To tackle these issues, we propose SurfaceFormer, a
pioneering Transformer-based mesh denoising framework. Our first contribution
is the development of a new representation known as Local Surface Descriptor,
which is crafted by establishing polar systems on each mesh face, followed by
sampling points from adjacent surfaces using geodesics. The normals of these
points are organized into 2D patches, mimicking images to capture local
geometric intricacies, whereas the poles and vertex coordinates are
consolidated into a point cloud to embody spatial information. This advancement
surmounts the hurdles posed by the irregular and non-Euclidean characteristics
of mesh data, facilitating a smooth integration with Transformer architecture.
Next, we propose a dual-stream structure consisting of a Geometric Encoder
branch and a Spatial Encoder branch, which jointly encode local geometry
details and spatial information to fully explore multimodal information for
mesh denoising. A subsequent Denoising Transformer module receives the
multimodal information and achieves efficient global feature aggregation
through self-attention operators. Our experimental evaluations demonstrate that
this novel approach outperforms existing state-of-the-art methods in both
objective and subjective assessments, marking a significant leap forward in
mesh denoising.

我们提出了一种名为 SurfaceFormer 的创新的基于 Transformer 的网格去噪框架，通过引入本地表面描述符以及双流结构和自注意力机制，实现了对网格噪声的有效去除。该方法在客观和主观评估中均超过了现有的最先进方法，标志着网格去噪领域的重要进展。

网格去噪变换器

Mesh Denoising Transformer

Transformers have recently gained increasing attention in computer vision.
However, existing studies mostly use Transformers for feature representation
learning, e.g. for image classification and dense predictions, and the
generalizability of Transformers is unknown. In this work, we further
investigate the possibility of applying Transformers for image matching and
metric learning given pairs of images. We find that the Vision Transformer
(ViT) and the vanilla Transformer with decoders are not adequate for image
matching due to their lack of image-to-image attention. Thus, we further design
two naive solutions, i.e. query-gallery concatenation in ViT, and query-gallery
cross-attention in the vanilla Transformer. The latter improves the
performance, but it is still limited. This implies that the attention mechanism
in Transformers is primarily designed for global feature aggregation, which is
not naturally suitable for image matching. Accordingly, we propose a new
simplified decoder, which drops the full attention implementation with the
softmax weighting, keeping only the query-key similarity computation.
Additionally, global max pooling and a multilayer perceptron (MLP) head are
applied to decode the matching result. This way, the simplified decoder is
computationally more efficient, while at the same time more effective for image
matching. The proposed method, called TransMatcher, achieves state-of-the-art
performance in generalizable person re-identification, with up to 6.1% and 5.7%
performance gains in Rank-1 and mAP, respectively, on several popular datasets.
Code is available at this https URL

本文认为转换器由于缺乏图像之间的关注而不适用于图像匹配，因此提出了一种简化的解码器，称为 TransMatcher，它在一些流行的数据集中实现了最先进的表现以进行泛化人员再识别。