Alessandro Simoni

With the growing use of Artificial Intelligence (AI) in remote sensing of mass movements, open and suitable datasets for model training and validation are increasingly needed. Although Differential Synthetic Aperture Radar Interferometry (DInSAR) is a widely used technique for studying mass movements, wrapped interferograms remain less exploited, and the importance of geomorphological expertise in their interpretation is not usually emphasised. In this work, we introduce a dataset of DInSAR wrapped phase signals designed to support the development of Deep Learning (DL) models for the automated detection and classification of active slow-moving mass movements. The dataset covers two selected wide areas in the Central European Alps and the Northern Apennines. It contains 4910 expert-annotated DInSAR wrapped phase signals derived from 92 Sentinel-1 interferograms with temporal baselines ranging from 6 days to 1 year, classified into nine distinct landslide and periglacial landform classes after careful geomorphological interpretation. This dataset is expected to support the scientific community in AI-based applications for mass movement research, while also serving as a benchmark for the generation of comparable datasets.

Synthetic dataset generation in Computer Vision, particularly for industrial applications, is still underexplored. Industrial defect segmentation, for instance, requires highly accurate labels, yet acquiring such data is costly and time-consuming. To address this challenge, we propose a novel diffusion-based pipeline for generating high-fidelity industrial datasets with minimal supervision. Our approach conditions the diffusion model on enriched bounding-box representations to produce precise segmentation masks, ensuring realistic and accurately localized defect synthesis. Compared to existing layout-conditioned generative methods, our approach improves defect consistency and spatial accuracy. We introduce two quantitative metrics to evaluate the effectiveness of our method and assess its impact on a downstream segmentation task trained on real and synthetic data. Our results demonstrate that diffusion-based synthesis can bridge the gap between artificial and real-world industrial data, fostering more reliable and cost-efficient segmentation models.

In recent years, there has been a notable surge in the significance attributed to technologies facilitating secure and efficient cohabitation and collaboration between humans and machines, with a particular interest in robotic systems. A pivotal element in actualizing this novel and challenging collaborative paradigm involves different technical tasks, including the comprehension of 3D poses exhibited by both humans and robots through the utilization of non-intrusive systems, such as cameras. In this scenario, the availability of vision-based systems capable of detecting in real-time the robot’s pose are needed as a first step towards a safe and effective interaction to, for instance, avoid collisions. Therefore, in this work, we propose a vision-based system, referred as D-SPDH, able to estimate the 3D robot pose. The system is based on double-branch architecture and depth data as a single input; any additional information regarding the state of internal encoders of the robot is not required. The working scenario is the Sim2Real, i.e ., the system is trained only with synthetic data and then tested on real sequences, thus eliminating the time-consuming acquisition and annotation procedures of real data, common phases in deep learning algorithms. Moreover, we introduce SimBa ⁺⁺ , a dataset featuring both synthetic and real sequences with new real-world double-arm movements, and that represents a challenging setting in which the proposed approach is tested. Experimental results show that our D-SPDH method achieves state-of-the-art and real-time performance, paving the way a possible future non-invasive systems to monitor human-robot interactions.

The three-dimensional representation of objects or scenes starting from a set of images has been a widely discussed topic for years and has gained additional attention after the diffusion of NeRF-based approaches. However, an underestimated prerequisite is the knowledge of camera poses or, more specifically, the estimation of the extrinsic calibration parameters. Although excellent general-purpose Structure-from-Motion methods are available as a pre-processing step, their computational load is high and they require a lot of frames to guarantee sufficient overlapping among the views. This paper introduces KRONC, a novel approach aimed at inferring view poses by leveraging prior knowledge about the object to reconstruct and its representation through semantic keypoints. With a focus on vehicle scenes, KRONC is able to estimate the position of the views as a solution to a light optimization problem targeting the convergence of keypoints' back-projections to a singular point. To validate the method, a specific dataset of real-world car scenes has been collected. Experiments confirm KRONC's ability to generate excellent estimates of camera poses starting from very coarse initialization. Results are comparable with Structure-from-Motion methods with huge savings in computation. Code and data will be made publicly available.

In this work, we propose a simple yet effective method to tackle the problem of imbalanced multi-class semantic segmentation in deep learning systems. One of the key properties for a good training set is the balancing among the classes. When the input distribution is heavily imbalanced in the number of instances, the learning process could be hindered or difficult to carry on. To this end, we propose a Dynamic Label Injection (DLI) algorithm to impose a uniform distribution in the input batch. Our algorithm computes the current batch defect distribution and re-balances it by transferring defects using a combination of Poisson-based seamless image cloning and cut-paste techniques. A thorough experimental section on the Magnetic Tiles dataset shows better results of DLI compared to other balancing loss approaches also in the challenging weakly-supervised setup. The code is available at https://github.com/covisionlab/dynamic-label-injection.git

Technologies to enable safe and effective collaboration and coexistence between humans and robots have gained significant importance in the last few years. A critical component useful for realizing this collaborative paradigm is the understanding of human and robot 3D poses using non-invasive systems. Therefore, in this paper, we propose a novel vision-based system leveraging depth data to accurately establish the 3D locations of skeleton joints. Specifically, we introduce the concept of Pose Nowcasting, denoting the capability of the proposed system to enhance its current pose estimation accuracy by jointly learning to forecast future poses. The experimental evaluation is conducted on two different datasets, providing accurate and real-time performance and confirming the validity of the proposed method on both the robotic and human scenarios.

Neural Radiance Fields (NeRFs) have gained widespread recognition as a highly effective technique for representing 3D reconstructions of objects and scenes derived from sets of images. Despite their efficiency, NeRF models can pose challenges in certain scenarios such as vehicle inspection, where the lack of sufficient data or the presence of challenging elements (e.g. reflections) strongly impact the accuracy of the reconstruction. To this aim, we introduce CarPatch, a novel synthetic benchmark of vehicles. In addition to a set of images annotated with their intrinsic and extrinsic camera parameters, the corresponding depth maps and semantic segmentation masks have been generated for each view. Global and part-based metrics have been defined and used to evaluate, compare, and better characterize some state-of-the-art techniques. The dataset is publicly released at https://aimagelab.ing.unimore.it/go/carpatch and can be used as an evaluation guide and as a baseline for future work on this challenging topic.