In this paper, we study the problem of shape part retrieval in the point cloud domain. Existing shape retrieval methods typically rely on the presence of a complete query object, but what if the part of interest is missing? We present the Part Retrieval Pipeline (PReP), which combines metric learning techniques with a trained classification model to evaluate the suitability of potential replacement parts from a database, within an application scenario aimed at circular economy. Through a progressively more difficult training procedure, PReP learns to recognize suitable parts based solely on shape context. Owing to its compact parameterization and low computational requirements, it can search a repository of tens of thousands of spare parts in just a few seconds. We also establish an alternative baseline approach for comparison, document the unique challenges associated with this task, and identify key design choices to address them.

We propose a novel point cloud U-Net diffusion architecture for 3-D generative modeling capable of generating high-quality and diverse 3-D shapes while maintaining fast generation times. Our network employs a dual-branch architecture, combining the high-resolution representations of points with the computational efficiency of sparse voxels. Our fastest variant outperforms all nondiffusion generative approaches on unconditional shape generation, the most popular benchmark for evaluating point cloud generative models, while our largest model achieves state-of-the-art results among diffusion methods, with a runtime approximately 70% of the previously state-of-the-art point-voxel diffusion (PVD), measured on the same hardware setting. Beyond unconditional generation, we perform extensive evaluations, including conditional generation on all categories of ShapeNet, demonstrating the scalability of our model to larger datasets, and implicit generation, which allows our network to produce high-quality point clouds on fewer timesteps, further decreasing the generation time. Finally, we evaluate the architecture's performance in point cloud completion and super-resolution. Our model excels in all tasks, establishing it as a state-of-the-art diffusion U-Net for point cloud generative modeling. The code is publicly available at https://github.com/JohnRomanelis/SPVD.

Solving jigsaw puzzles, a long-standing challenge in both human cognition and artificial intelligence, has seen significant progress with modern computer vision techniques. In this paper, we introduce a diffusion-based framework for jigsaw puzzle reconstruction, leveraging denoising diffusion models to iteratively refine piece placements. Unlike prior methods that rely on anchored reference pieces and relative positioning, our approach directly regresses absolute positions, making it more flexible and generalizable. Additionally, we extend puzzle-solving beyond square pieces by incorporating polygonal partitions and employ DDIM for efficient inference. Our modular pipeline is adaptable to various puzzle formulations, and we demonstrate its effectiveness by achieving state-of-the-art performance on the JPwLEG benchmark.

We propose a novel framework, Guided Sparse Point-Voxel Diffusion (G-SPVD), for Point Cloud generation guided from a single visual input - either an image or a rough hand-drawn sketch, both from an unknown viewing angle. G-SPVD combines a Vision Transformer with a Diffusion Model that iteratively forms a noisy set of points to match the requested input. Our quantitative evaluation demonstrates that our framework achieves state-of-the-art results compared to other methods in single-image reconstruction on the ShapeNet dataset. Moreover, despite the reduced information available in sketchbased inputs, our sketch-guided model still attains competitive reconstruction metrics. We present several qualitative results for both tasks to further illustrate the effectiveness of our method. Finally, we evaluate our method on unconditional generation, demonstrating that our model can generate shapes with quality and diversity on par with the current state-of-the-art. Our code will be released upon publication.

Gesture recognition is a tool to enable novel interactions with different techniques and applications, like Mixed Reality and Virtual Reality environments. With all the recent advancements in gesture recognition from skeletal data, it is still unclear how well state-of-the-art techniques perform in a scenario using precise motions with two hands. This paper presents the results of the SHREC 2024 contest organized to evaluate methods for their recognition of highly similar hand motions using the skeletal spatial coordinate data of both hands. The task is the recognition of 7 motion classes given their spatial coordinates in a frame-by-frame motion. The skeletal data has been captured using a Vicon system and pre-processed into a coordinate system using Blender and Vicon Shogun Post. We created a small, novel dataset with a high variety of durations in frames. This paper shows the results of the contest, showing the techniques created by the 5 research groups on this challenging task and comparing them to our baseline method.