De/construction sites: Romans and the digital playground

The Roman world as attested to archaeologically and as interacted with today has its expression in a great many computational and other media. The place of visualisation within this has been paramount. This paper argues that the process of digitally constructing the Roman world and the exploration of the resultant models are useful methods for interpretation and influential factors in the creation of a popular Roman aesthetic. Furthermore, it suggests ways in which novel computational techniques enable the systematic deconstruction of such models, in turn re-purposing the many extant representations of Roman architecture and material culture

Extreme Two-View Geometry From Object Poses with Diffusion Models

Human has an incredible ability to effortlessly perceive the viewpoint difference between two images containing the same object, even when the viewpoint change is astonishingly vast with no co-visible regions in the images. This remarkable skill, however, has proven to be a challenge for existing camera pose estimation methods, which often fail when faced with large viewpoint differences due to the lack of overlapping local features for matching. In this paper, we aim to effectively harness the power of object priors to accurately determine two-view geometry in the face of extreme viewpoint changes. In our method, we first mathematically transform the relative camera pose estimation problem to an object pose estimation problem. Then, to estimate the object pose, we utilize the object priors learned from a diffusion model Zero123 to synthesize novel-view images of the object. The novel-view images are matched to determine the object pose and thus the two-view camera pose. In experiments, our method has demonstrated extraordinary robustness and resilience to large viewpoint changes, consistently estimating two-view poses with exceptional generalization ability across both synthetic and real-world datasets. Code will be available at https://github.com/scy639/Extreme-Two-View-Geometry-From-Object-Poses-with-Diffusion-Models

View planning for efficient contour-based 3D object recognition

This paper presents a method for capture planning in view based 3D recognition. Views are represented by their contours, encoded into curvature functions, which are reduced into compact feature vectors by Principal Component Analysis. These vectors are very resistant against transformations, so they can be assumed to be distributed over the surface of a sphere with the object in its center. After clustering these vectors, 3D objects are represented via Hidden Markov Models where classes are states. To recognize an object in a minimum number of steps, we propose to align candidate cluster representations and then subtracting their cluster maps to decide in which locations they differ the most. Then, a TSP is used to decide in which order these distinctive locations are visited. The proposed approach has been successfully tested with several artificial 3D object databases, even though it still presents some errors in objects with strong symmetries.Ministerio de Ciencia e Innovación (MICINN) project TEC-2008-06734 Junta de Andalucia (JA) project TIC-0310

Urban intersection classification: a comparative analysis

Understanding the scene in front of a vehicle is crucial for self-driving vehicles and Advanced Driver Assistance Systems, and in urban scenarios, intersection areas are one of the most critical, concentrating between 20% to 25% of road fatalities. This research presents a thorough investigation on the detection and classification of urban intersections as seen from onboard front-facing cameras. Different methodologies aimed at classifying intersection geometries have been assessed to provide a comprehensive evaluation of state-of-the-art techniques based on Deep Neural Network (DNN) approaches, including single-frame approaches and temporal integration schemes. A detailed analysis of most popular datasets previously used for the application together with a comparison with ad hoc recorded sequences revealed that the performances strongly depend on the field of view of the camera rather than other characteristics or temporal-integrating techniques. Due to the scarcity of training data, a new dataset is created by performing data augmentation from real-world data through a Generative Adversarial Network (GAN) to increase generalizability as well as to test the influence of data quality. Despite being in the relatively early stages, mainly due to the lack of intersection datasets oriented to the problem, an extensive experimental activity has been performed to analyze the individual performance of each proposed systems.European Commissio

Alternative Methods

ABSTRACT In the realm of precognitive artmaking, the artist’s role is that of an antenna. One must be receptive to the subtle, invisible flow of creative novelty in order to participate in the involuntary channeling of new ideas, new processes, and alternative methods of creative production. Carving out new territory within the realm of static art is a primary objective for my artistic process. By utilizing digital fabrication tools, paired with my affinity for intricate craft and optical metagrobolization, I have created a body of work that invents alternative processes and unique aesthetic languages. Digital imaging, digital modeling and digital fabrication offer unique opportunities to alleviate some of the manual burden of art making by relegating repetitive and/or strenuous tasks to machine operations. I am developing ways to reduce the physical burden to my hands of my artmaking by utilizing computers and machines. Digital imaging, modeling and fabrication also present the opportunity for artists to explore precision of interrelated parts in ways previously unachievable. I am developing the artistic possibilities of working with fitment, micro-accuracy of 3D modeling, height maps and tool paths, micro-accuracy of stereolithography 3D printing and the micro-accuracy of CNC machine operations