One of the primary challenges inherent in utilizing deep learning models is the scarcity and accessibility hurdles associated with acquiring datasets of sufficient size to facilitate effective training of these networks. This is particularly significant in object detection, shape completion, and fracture assembly. Instead of scanning a large number of real-world fragments, it is possible to generate massive datasets with synthetic pieces. However, realistic fragmentation is computationally intensive in the preparation (e.g., pre-factured models) and generation. Otherwise, simpler algorithms such as Voronoi diagrams provide faster processing speeds at the expense of compromising realism. In this context, it is required to balance computational efficiency and realism. This paper introduces a GPU-based framework for the massive generation of voxelized fragments derived from high-resolution 3D models, specifically prepared for their utilization as training sets for machine learning models. This rapid pipeline enables controlling how many pieces are produced, their dispersion and the appearance of subtle effects such as erosion. We have tested our pipeline with an archaeological dataset, producing more than 1M fragmented pieces from 1,052 Iberian vessels ( Github ). Although this work primarily intends to provide pieces as implicit data represented by voxels, triangle meshes and point clouds can also be inferred from the initial implicit representation. To underscore the unparalleled benefits of CPU and GPU acceleration in generating vast datasets, we compared against a realistic fragment generator that highlights the potential of our approach, both in terms of applicability and processing time. We also demonstrate the synergies between our pipeline and realistic simulators, which frequently cannot select the number and size of resulting pieces. To this end, a deep learning model was trained over realistic fragments and our dataset, showing similar results. • GPU-based fragmentation of voxelized artefacts for simulating brittle fractures. • Generation of large datasets for training machine learning models. • Definition of how many fragments are produced, their dispersion and appearance. • Publication of a dataset comprising more than 1M fragments.

Accurate completion of archaeological artifacts is a critical aspect in several archaeological studies, including documentation of variations in style, inference of chronological and ethnic groups, and trading routes trends, among many others. However, most available pottery is fragmented, leading to missing textural and morphological cues. Currently, the reassembly and completion of fragmented ceramics is a daunting and time-consuming task, done almost exclusively by hand, which requires the physical manipulation of the fragments. To overcome the challenges of manual reconstruction, reduce the materials' exposure and deterioration, and improve the quality of reconstructed samples, we present IberianVoxel, a novel 3D Autoencoder Generative Adversarial Network (3D AE-GAN) framework tested on an extensive database with complete and fragmented references. We generated a collection of 1001 3D voxelized samples and their fragmented references from Iberian wheel-made pottery profiles. The fragments generated are stratified into different size groups and across multiple pottery classes. Lastly, we provide quantitative and qualitative assessments to measure the quality of the reconstructed voxelized samples by our proposed method and archaeologists' evaluation.

The widespread use of LiDAR technology in a multitude of domains has produced a growing availability of massive high-resolution point datasets that demand new approaches for efficient organization and storage, filtering using different spatio-temporal criteria, selective/progressive visualization, processing and analysis, and collaborative editing. Ideally, LiDAR data coming from multiple sources and organized in different datasets should be accessible in a simple, uniform, and ubiquitous way to comply with the FAIR principle proposed by the Open Geospatial Consortium: Findable, Accessible, Interoperable, and Reusable. With this goal in mind, we present SPSLiDAR, a conceptual model with a simple interface for repositories of LiDAR data that can be adapted to the needs of different applications. SPSLiDAR includes aspects, such as the arrangement of related datasets into workspaces on a world scale, support for overlapping datasets with different resolutions or acquired at different times, and hierarchical organization of point data, enabling levels of detail and selective download. We also describe in detail an implementation of this model aimed at visualization and downloading of large datasets using the MongoDB database. Finally, we show some experimental results of this implementation using real data, such as its space requirements, upload latency, access latency, and throughput.

Abstract In this work we present new point inclusion algorithms for non‐convex polygons. These algorithms do not perform any pre‐processing or any type of decomposition nor features classification, which makes them especially suitable for deformable or moving polygons. The algorithms are more accurate and robust than others in the sense that they consider the inclusion of the point in the vertices and edges of the polygon, and deal with the special cases correctly. In order to perform this inclusion test efficiently, they use the sign of the barycentric coordinates of the test point with regard to the triangles formed by the edges and an origin that depends on the test point. This set of triangles, which is a special simplicial covering of the polygon, is constructed after a transformation of the polygon that simplifies the calculations involved in the inclusion test. Then, an appropriate ordering of the rejection tests allows us to optimize this method. Our algorithms have been tested for robustness and compared with ray‐crossing methods, showing a significant improvement.

Abstract Collision detection (CD) among complex objects in motion is an open question because of its algorithmic complexity. In this paper, we present a CD algorithm between a particle and a complex rigid solid. In order to represent solids, we use a simplicial covering scheme by means of 3‐simplices. With this representation system, complex polyhedra and closed triangle meshes can be represented and used in CD with a particle. A particle can be represented by a point, where the real dimensions of the particle are not important. This CD algorithm has been extended for a particle represented by a sphere which in turn represents an approximation to the particle dimensions. In order to efficiently classify the particles and the simplices of the polyhedra covering, we use a new hierarchical data structure named tetra‐tree that decomposes the space into tetra‐cones. These algorithms also use bounding volumes and temporal and geometric coherence, as well as incremental calculations in order to determine the collision in an efficient and exact way. Because of the use of sign operations with signed volumes and barycentric coordinates, we conclude that operations carried out using this method are more robust and efficient than those used in classic algorithms.