Recent advances in deep learning have significantly transformed the field of 3D shape generation, enabling the synthesis of complex, diverse, and semantically meaningful 3D objects. This survey provides a comprehensive overview of the current state-of-the-art in 3D shape generation, organizing the discussion around three core components: shape representations, generative modeling approaches, and evaluation protocols. We begin by categorizing 3D representations into explicit, implicit, and hybrid setups, highlighting their structural properties, advantages, and limitations. Next, we review a wide range of generation methods, focusing on feedforward architectures. We further summarize commonly used datasets and evaluation metrics that assess fidelity, diversity, and realism of generated shapes. Finally, we identify open challenges and outline future research directions that could drive progress in controllable, efficient, and high-quality 3D shape generation. This survey aims to serve as a valuable reference for researchers and practitioners seeking a structured and in-depth understanding of this rapidly evolving field.

This article explores the use of recent generative AI algorithms for repairing cultural heritage objects, leveraging a conditional diffusion model designed to reconstruct 3D point clouds effectively. Our study evaluates the model’s performance across general and cultural heritage-specific settings. Results indicate that, with considerations for object variability, the diffusion model can accurately reproduce cultural heritage geometries. Despite encountering challenges like data diversity and outlier sensitivity, the model demonstrates significant potential in artifact restoration research. This work lays groundwork for advancing restoration methodologies for ancient artifacts using AI technologies (The dataset is available in: https://github.com/PJaramilloV/Precolombian-Dataset, and the code in https://github.com/PJaramilloV/pcdiff-method).

Partial retrieval is a long-standing problem in the 3D Object Retrieval community. Its main difficulties arise from how to define 3D local descriptors in a way that makes them effective for partial retrieval and robust to common real-world issues, such as occlusion, noise, or clutter, when dealing with 3D data. This SHREC track is based on the newly proposed ShapeBench benchmark to evaluate the matching performance of local descriptors. We propose an experiment consisting of three increasing levels of difficulty, where we combine different filters to simulate real-world issues related to the partial retrieval task. Our main findings show that classic 3D local descriptors like Spin Image are robust to several of the tested filters (and their combinations), but more recent learned local descriptors like GeDI can be competitive for some specific filters. Finally, no 3D local descriptor was able to successfully handle the hardest level of difficulty. • We evaluate the robustness of local 3D descriptors for partial shape retrieval using a novel benchmark—ShapeBench—under progressively challenging conditions simulating real-world degradations (e.g., clutter, occlusion, noise, remeshing). • Our analysis shows that classic hand-crafted descriptors like Spin Image consistently outperform more recent learned descriptors under high levels of occlusion and noise. • No existing local 3D descriptor was found to be reliably effective under the most challenging scenarios combining multiple perturbations, highlighting an open problem in robust partial 3D retrieval.

Unlike image or text domains that benefit from an abundance of large-scale datasets, point cloud learning techniques frequently encounter limitations due to the scarcity of extensive datasets. To overcome this limitation, we present Symmetria, a formula-driven dataset that can be generated at any arbitrary scale. By construction, it ensures the absolute availability of precise ground truth, promotes data-efficient experimentation by requiring fewer samples, enables broad generalization across diverse geometric settings, and offers easy extensibility to new tasks and modalities. Using the concept of symmetry, we create shapes with known structure and high variability, enabling neural networks to learn point cloud features effectively. Our results demonstrate that this dataset is highly effective for point cloud self-supervised pre-training, yielding models with strong performance in downstream tasks such as classification and segmentation, which also show good few-shot learning capabilities. Additionally, our dataset can support fine-tuning models to classify real-world objects, highlighting our approach’s practical utility and application. We also introduce a challenging task for symmetry detection and provide a benchmark for baseline comparisons. A significant advantage of our approach is the public availability of the dataset, the accompanying code, and the ability to generate very large collections, promoting further research and innovation in point cloud learning.

We present a regularized reconstruction model to address video summarization. We assume a video can be viewed as a subspace formed by a selected subset of frames, with frames represented as a sparse linear combination of these selected frames. Our method selects frames that contribute to the reconstruction of the entire video by leveraging both the structure and similarity between sparse codes. The structure is provided by groups of frames showing subtle or significant changes, while the similarity ensures a balanced contribution from the frames in these groups. We propose an optimization problem to produce a sparse representation capturing the relevance of each frame, solving this non-smooth problem using proximal gradient methods. We compared our method with state-of-the-art methods through experiments using a standard dataset and a new dataset for volleyball phase analysis. Our results demonstrate that our method produces effective summaries and outperforms existing methods.