Direct matching of 2D keypoints in an input image to a 3D point cloud of the
scene without requiring visual descriptors has garnered increased interest due
to its lower memory requirements, inherent privacy preservation, and reduced
need for expensive 3D model maintenance compared to visual descriptor-based
methods. However, existing algorithms often compromise on performance,
resulting in a significant deterioration compared to their descriptor-based
counterparts. In this paper, we introduce DGC-GNN, a novel algorithm that
employs a global-to-local Graph Neural Network (GNN) that progressively
exploits geometric and color cues to represent keypoints, thereby improving
matching robustness. Our global-to-local procedure encodes both Euclidean and
angular relations at a coarse level, forming the geometric embedding to guide
the local point matching. We evaluate DGC-GNN on both indoor and outdoor
datasets, demonstrating that it not only doubles the accuracy of the
state-of-the-art descriptor-free algorithm but, also, substantially narrows the
performance gap between descriptor-based and descriptor-free methods. The code
and trained models will be made publicly available.

本论文介绍了一种名为 DGC-GNN 的新算法，它采用全局到局部的图神经网络来逐步利用几何和颜色线索来表示关键点，从而提高匹配的鲁棒性。DGC-GNN 不仅使无描述符算法的精度翻倍，而且还显著缩小了基于描述符和无描述符方法之间的性能差距。

DGC-GNN: 基于几何颜色图神经网络的无特征描述符 2D-3D 匹配

DGC-GNN: Descriptor-free Geometric-Color Graph Neural Network for 2D-3D  Matching

In the light of recent analyses on privacy-concerning scene revelation from
visual descriptors, we develop descriptors that conceal the input image
content. In particular, we propose an adversarial learning framework for
training visual descriptors that prevent image reconstruction, while
maintaining the matching accuracy. We let a feature encoding network and image
reconstruction network compete with each other, such that the feature encoder
tries to impede the image reconstruction with its generated descriptors, while
the reconstructor tries to recover the input image from the descriptors. The
experimental results demonstrate that the visual descriptors obtained with our
method significantly deteriorate the image reconstruction quality with minimal
impact on correspondence matching and camera localization performance.

我们提出了基于对抗学习框架的视觉描述符，可防止图像重构并保持匹配精度。实验表明，我们的方法获得的视觉描述符显着降低了图像重构质量，对应匹配和相机定位性能影响较小。

通过对抗学习实现内容隐藏的视觉描述符

NinjaDesc: Content-Concealing Visual Descriptors via Adversarial  Learning

We study the use of deep features extracted from a pretrained Vision
Transformer (ViT) as dense visual descriptors. We observe and empirically
demonstrate that such features, when extractedfrom a self-supervised ViT model
(DINO-ViT), exhibit several striking properties, including: (i) the features
encode powerful, well-localized semantic information, at high spatial
granularity, such as object parts; (ii) the encoded semantic information is
shared across related, yet different object categories, and (iii) positional
bias changes gradually throughout the layers. These properties allow us to
design simple methods for a variety of applications, including co-segmentation,
part co-segmentation and semantic correspondences. To distill the power of ViT
features from convoluted design choices, we restrict ourselves to lightweight
zero-shot methodologies (e.g., binning and clustering) applied directly to the
features. Since our methods require no additional training nor data, they are
readily applicable across a variety of domains. We show by extensive
qualitative and quantitative evaluation that our simple methodologies achieve
competitive results with recent state-of-the-art supervised methods, and
outperform previous unsupervised methods by a large margin. Code is available
in dino-vit-features.github.io.

本文研究使用预训练的 Vision Transformer (ViT) 提取的深度特征作为密集的视觉描述符，提出了基于无监督 DINO-ViT 模型提取特征的简单方法，可用于各种领域的相关应用，包括共分割、语义对应等。经过大量定量和定性分析得出了符合竞争性的结果，并且较之前的无监督方法有了很大的提高。