arXiv subjects

Luigi Lella

Publications and source records attributed to Luigi Lella.

4 recordsLinked to original sources

arXiv2025

SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark

We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the task is to produce a voxel-based Anomaly Volume. Moreover, SiM3D focuses on a scenario of high interest in manufacturing: single-instance anomaly detection, where only one object, either real or synthetic, is available for training. In this respect, SiM3D stands out as the first ADS benchmark that addresses the challenge of generalising from synthetic training data to real test data. SiM3D includes a novel multimodal multiview dataset acquired using top-tier industrial sensors and robots. The dataset features multiview high-resolution images (12 Mpx) and point clouds (7M points) for 333 instances of eight types of objects, alongside a CAD model for each type. We also provide manually annotated 3D segmentation GTs for anomalous test samples. To establish reference baselines for the proposed multiview 3D ADS task, we adapt prominent singleview methods and assess their performance using novel metrics that operate on Anomaly Volumes.

cs.CV↗

arXiv2020

Shooting Labels: 3D Semantic Labeling by Virtual Reality

Availability of a few, large-size, annotated datasets, like ImageNet, Pascal VOC and COCO, has lead deep learning to revolutionize computer vision research by achieving astonishing results in several vision tasks.We argue that new tools to facilitate generation of annotated datasets may help spreading data-driven AI throughout applications and domains. In this work we propose Shooting Labels, the first 3D labeling tool for dense 3D semantic segmentation which exploits Virtual Reality to render the labeling task as easy and fun as playing a video-game. Our tool allows for semantically labeling large scale environments very expeditiously, whatever the nature of the 3D data at hand (e.g. point clouds, mesh). Furthermore, Shooting Labels efficiently integrates multiusers annotations to improve the labeling accuracy automatically and compute a label uncertainty map. Besides, within our framework the 3D annotations can be projected into 2D images, thereby speeding up also a notoriously slow and expensive task such as pixel-wise semantic labeling. We demonstrate the accuracy and efficiency of our tool in two different scenarios: an indoor workspace provided by Matterport3D and a large-scale outdoor environment reconstructed from 1000+ KITTI images.

cs.CV↗

arXiv2007

A New Model for the Organizational Knowledge Life Cycle

Actual organizations, in particular the ones which operate in evolving and distributed environments, need advanced frameworks for the management of the knowledge life cycle. These systems have to be based on the social relations which constitute the pattern of collaboration ties of the organization. We demonstrate here, with the aid of a model taken from the theory of graphs, that it is possible to provide the conditions for an effective knowledge management. A right way could be to involve the actors with the highest betweeness centrality in the generation of discussion groups. This solution allows the externalization of tacit knowledge, the preservation of knowledge and the raise of innovation processes.

physics.gen-ph↗

arXiv2007

Evolutionary Neural Gas (ENG): A Model of Self Organizing Network from Input Categorization

Despite their claimed biological plausibility, most self organizing networks have strict topological constraints and consequently they cannot take into account a wide range of external stimuli. Furthermore their evolution is conditioned by deterministic laws which often are not correlated with the structural parameters and the global status of the network, as it should happen in a real biological system. In nature the environmental inputs are noise affected and fuzzy. Which thing sets the problem to investigate the possibility of emergent behaviour in a not strictly constrained net and subjected to different inputs. It is here presented a new model of Evolutionary Neural Gas (ENG) with any topological constraints, trained by probabilistic laws depending on the local distortion errors and the network dimension. The network is considered as a population of nodes that coexist in an ecosystem sharing local and global resources. Those particular features allow the network to quickly adapt to the environment, according to its dimensions. The ENG model analysis shows that the net evolves as a scale-free graph, and justifies in a deeply physical sense- the term gas here used.

physics.gen-ph↗