Deep Shape Matching

We cast shape matching as metric learning with convolutional networks. We break the end-to-end process of image representation into two parts. Firstly, well established efficient methods are chosen to turn the images into edge maps. Secondly, the network is trained with edge maps of landmark images, which are automatically obtained by a structure-from-motion pipeline. The learned representation is evaluated on a range of different tasks, providing improvements on challenging cases of domain generalization, generic sketch-based image retrieval or its fine-grained counterpart. In contrast to other methods that learn a different model per task, object category, or domain, we use the same network throughout all our experiments, achieving state-of-the-art results in multiple benchmarks.Comment: ECCV 201

LiDAR-Based Place Recognition For Autonomous Driving: A Survey

LiDAR-based place recognition (LPR) plays a pivotal role in autonomous driving, which assists Simultaneous Localization and Mapping (SLAM) systems in reducing accumulated errors and achieving reliable localization. However, existing reviews predominantly concentrate on visual place recognition (VPR) methods. Despite the recent remarkable progress in LPR, to the best of our knowledge, there is no dedicated systematic review in this area. This paper bridges the gap by providing a comprehensive review of place recognition methods employing LiDAR sensors, thus facilitating and encouraging further research. We commence by delving into the problem formulation of place recognition, exploring existing challenges, and describing relations to previous surveys. Subsequently, we conduct an in-depth review of related research, which offers detailed classifications, strengths and weaknesses, and architectures. Finally, we summarize existing datasets, commonly used evaluation metrics, and comprehensive evaluation results from various methods on public datasets. This paper can serve as a valuable tutorial for newcomers entering the field of place recognition and for researchers interested in long-term robot localization. We pledge to maintain an up-to-date project on our website https://github.com/ShiPC-AI/LPR-Survey.Comment: 26 pages,13 figures, 5 table

Unsupervised Feature Extraction – A CNN-Based Approach

Working with large quantities of digital images can often lead to prohibitive computational challenges due to their massive number of pixels and high dimensionality. The extraction of compressed vectorial representations from images is therefore a task of vital importance in the field of computer vision. In this paper, we propose a new architecture for extracting such features from images in an unsupervised manner, which is based on convolutional neural networks. The model is referred to as the Unsupervised Convolutional Siamese Network (UCSN), and is trained to embed a set of images in a vector space, such that local distance structure in the space of images is approximately preserved. We compare the UCSN to several classical methods by using the extracted features as input to a classification system. Our results indicate that the UCSN produces vectorial representations that are suitable for classification purposes