In this research paper, we introduce a novel approach to enhance the performance of 2D feature matching and pose estimation through the integration of a hierarchical attention mechanism and knowledge distillation. Our proposed hierarchical attention mechanism operates at multiple scales, enabling both global context awareness and precise matching of 2D features, which is crucial for various computer vision tasks. To further improve our model’s performance, we incorporate insights from an existing model PixLoc (Sarlin et al., 2021) through knowledge distillation, effectively acquiring its behavior and capabilities by ignoring dynamic objects. SAM-Net outperforms state-of-the-art methods, validated on both indoor and outdoor public datasets. For the indoor dataset, our approach achieves remarkable AUC (5°/10°/20°) scores of 55.31/71.70/83.37. Similarly, for the outdoor dataset, we demonstrate outstanding AUC values of 26.01/46.44/63.61. Furthermore, SAM-Net achieves top ranking among published methods in two public visual localization benchmarks, highlighting the real benefits of the proposed method. The code and test suite can be accessed at link.1

The problem of vine disease detection (VDD) was addressed in a number of research papers, however, a generic solution is not yet available for this task in the community.The region of interest segmentation and object detection tasks are often complementary.A similar situation is encountered in VDD applications as well, in which crop or leaf detection can be done via instance segmentation techniques as well.The focus of this work is to validate the most suitable methods from the main literature on vine leaf segmentation and disease detection on a custom dataset containing leaves both from the laboratory environment and cropped from images in the field.We tested five promising methods including the Otsu's thresholding, Mask R-CNN, MobileNet, SegNet, and Feature Pyramid Network variants.The results of the comparison are available in Table I summarizing the accuracy and runtime of different methods.

A novel method is proposed for the absolute pose estimation of a central 2D camera with respect to 3D depth data without the use of any dedicated calibration pattern or explicit point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the 3D sensing device and a central camera is used to capture the 2D images. Both the perspective and omnidirectional central cameras are handled within a single generic camera model. Pose estimation is formulated as a 2D-3D nonlinear shape registration task which is solved without point correspondences or complex similarity metrics. It relies on a set of corresponding planar regions, and the pose parameters are obtained by solving an overdetermined system of nonlinear equations. The efficiency and robustness of the proposed method were confirmed on both large scale synthetic data and on real data acquired from various types of sensors.

A workflow is proposed for Cultural Heritage applications in which the fusion of 3D and 2D visual data is required. Using data acquired by cheap, standard devices, like a 3D scanner having a low quality 2D camera in it, and a high resolution DSLR camera, one can produce high quality color calibrated 3D model for documenting purpose. The proposed processing workflow combines a novel region based calibration method with an ICP alignment used for refining the results. It works on 3D data, that do not necessarily contain intensity information in them, and 2D images of a calibrated camera. These can be acquired with commercial 3D scanners and color cameras without any special constraint. In contrast with the typical solutions, the proposed method is not using any calibration patterns or markers. The efficiency and robustness of the proposed calibration method has been confirmed on both synthetic and real data.

We consider an online planning algorithm for partially observable Markov decision processes (POMDPs), called Anytime Error Minimization Search 2 (AEMS2). Despite the considerable success it has enjoyed in robotics and other problems, no quantitative analysis exists of the relationship between its near-optimality and the computation invested. Exploiting ideas from fully-observable MDP planning, we provide here such an analysis, in which the relationship is modulated via a measure of problem complexity called near-optimality exponent. We illustrate the exponent for some interesting POMDP structures, and examine the role of the informative heuristics used by AEMS2 in the guarantees. In the second part of the paper, we introduce a domestic assistance problem in which a robot monitors partially observable switches and turns them off if needed. AEMS2 successfully solves this task in real experiments, and also works better than several state of the art planners in simulation comparisons.