We present a novel end-to-end algorithm (PoCo) for the indoor RGB-D place
recognition task, aimed at identifying the most likely match for a given query
frame within a reference database. The task presents inherent challenges
attributed to the constrained field of view and limited range of perception
sensors. We propose a new network architecture, which generalizes the recent
Context of Clusters (CoCs) to extract global descriptors directly from the
noisy point clouds through end-to-end learning. Moreover, we develop the
architecture by integrating both color and geometric modalities into the point
features to enhance the global descriptor representation. We conducted
evaluations on public datasets ScanNet-PR and ARKit with 807 and 5047
scenarios, respectively. PoCo achieves SOTA performance: on ScanNet-PR, we
achieve R@1 of 64.63%, a 5.7% improvement from the best-published result CGis
(61.12%); on Arkit, we achieve R@1 of 45.12%, a 13.3% improvement from the
best-published result CGis (39.82%). In addition, PoCo shows higher efficiency
than CGis in inference time (1.75X-faster), and we demonstrate the
effectiveness of PoCo in recognizing places within a real-world laboratory
environment.

我们提出了一种新的端到端算法（PoCo）用于室内 RGB-D 场所识别任务，旨在识别给定查询帧在参考数据库中最可能的匹配。我们提出了一种新的网络架构，通过端到端学习直接从噪声点云中提取全局描述符，并将颜色和几何模态集成到点特征中以增强全局描述符表示。我们在公共数据集 ScanNet-PR 和 ARKit 上进行了评估，结果显示 PoCo 在性能和效率方面均优于已发表的最佳结果 CGis，并证明了 PoCo 在识别实际实验室环境中的位置方面的有效性。

PoCo：RGBD 室内地点识别的点上下文聚类

PoCo: Point Context Cluster for RGBD Indoor Place Recognition

The application of eye-tracking techniques in medical image analysis has
become increasingly popular in recent years. It collects the visual search
patterns of the domain experts, containing much important information about
health and disease. Therefore, how to efficiently integrate radiologists' gaze
patterns into the diagnostic analysis turns into a critical question. Existing
works usually transform gaze information into visual attention maps (VAMs) to
supervise the learning process. However, this time-consuming procedure makes it
difficult to develop end-to-end algorithms. In this work, we propose a novel
gaze-guided graph neural network (GNN), GazeGNN, to perform disease
classification from medical scans. In GazeGNN, we create a unified
representation graph that models both the image and gaze pattern information.
Hence, the eye-gaze information is directly utilized without being converted
into VAMs. With this benefit, we develop a real-time, real-world, end-to-end
disease classification algorithm for the first time and avoid the noise and
time consumption introduced during the VAM preparation. To our best knowledge,
GazeGNN is the first work that adopts GNN to integrate image and eye-gaze data.
Our experiments on the public chest X-ray dataset show that our proposed method
exhibits the best classification performance compared to existing methods.

本文中提出了一种新的注视引导的图神经网络（GazeGNN），用于对医学扫描进行疾病分类，该方法不需要将目光信息转换为视觉注意力图；该方法在公共胸部 X-ray 数据集上实验，相较于现有方法，本方法取得了最佳分类性能。

GazeGNN: 一种用于疾病分类的注视导向图神经网络

GazeGNN: A Gaze-Guided Graph Neural Network for Disease Classification

We present a novel, end-to-end learnable, multiview 3D point cloud
registration algorithm. Registration of multiple scans typically follows a
two-stage pipeline: the initial pairwise alignment and the globally consistent
refinement. The former is often ambiguous due to the low overlap of neighboring
point clouds, symmetries and repetitive scene parts. Therefore, the latter
global refinement aims at establishing the cyclic consistency across multiple
scans and helps in resolving the ambiguous cases. In this paper we propose, to
the best of our knowledge, the first end-to-end algorithm for joint learning of
both parts of this two-stage problem. Experimental evaluation on well accepted
benchmark datasets shows that our approach outperforms the state-of-the-art by
a significant margin, while being end-to-end trainable and computationally less
costly. Moreover, we present detailed analysis and an ablation study that
validate the novel components of our approach. The source code and pretrained
models are publicly available under
this https URL

本文介绍了一种新颖的端到端可学习的多视角三维点云拼接算法，该算法集成了两阶段流程（成对对准和全局优化），并且是当前领先技术的改进。