Computer Graphics Forum最新文献

Evolving measuring and computing capabilities, along with increasingly complex problems or models, are resulting in a new type of dataset: second-generation ensemble data. Like first-generation ensemble data, these data consist of large-scale, repeated measurements of the same process or phenomenon, and often have a spatial component. Unlike older datasets, they typically require the extraction and aggregation of novel complex features, which may be generated through direct measurements rather than simulations, and appear in a wider range of application domains, including neuroscience. We describe an interactive visual analysis solution for this type of second-generation ensemble data, related to the study and planning of surgical interventions in epilepsy treatment. As part of this solution, we introduce a dynamic community abstraction in conjunction with analysis algorithms for feature extraction and aggregation, registration techniques to correlate and project sample data, and custom visual encodings to support the analysis of conserved network patterns. A quantitative and qualitative evaluation with domain experts at four sites demonstrates the effectiveness of this solution. We discuss this approach and solution in the context of second-generation ensemble data analysis, along with the challenges of working with this type of data.

Symptom modelling in head and neck cancer is challenged by the complexity of heterogeneous patient data, leading to an interest in deep learning approaches. Although Long Short-Term Memory Networks (LSTMs) have shown great results in patient risk prediction, their low interpretability requires data modellers to collaborate with clinical experts to validate the results. We present L-VISP, a human-machine solution that uses visual analytics for LSTM modelling in clinical research. L-VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context. We evaluate L-VISP with data modellers and a clinical oncologist and present the takeaways from this multidisciplinary collaboration.

Topological properties play a crucial role in the analysis, reconstruction, and generation of 3D shapes. Yet, most existing research focuses primarily on geometric features, due to the lack of effective representations for topology. In this paper, we introduce TopoGen, a method that extracts both discrete and continuous topological descriptors–Betti numbers and persistence points–using persistent homology. These features provide robust characterizations of 3D shapes in terms of their topology. We incorporate them as conditional guidance in generative models for 3D shape synthesis, enabling topology-aware generation from diverse inputs such as sparse and partial point clouds, as well as sketches. Furthermore, by modifying persistence points, we can explicitly control and alter the topology of generated shapes. Experimental results demonstrate that TopoGen enhances both diversity and controllability in 3D generation by embedding global topological structure into the synthesis process.

Recent deep learning methods can generate diverse graphic design layouts efficiently. However, these methods often create layouts with flaws, such as misalignment, unwanted overlaps, and unsatisfied containment. To tackle this issue, we propose an optimization-based method called LayoutRectifier, which gracefully rectifies auto-generated graphic design layouts to reduce these flaws while minimizing deviation from the generated layout. The core of our method is a two-stage optimization. First, we utilize grid systems, which professional designers commonly use to organize elements, to mitigate misalignments through discrete search. Second, we introduce a novel box containment function designed to adjust the positions and sizes of the layout elements, preventing unwanted overlapping and promoting desired containment. We evaluate our method on content-agnostic and content-aware layout generation tasks and achieve better-quality layouts that are more suitable for downstream graphic design tasks. Our method complements learning-based layout generation methods and does not require additional training.

Point cloud completion is vital for accurate 3D reconstruction, yet real world scans frequently exhibit large structural gaps that compromise recovery. Meanwhile, in 2D vision, VAR (Visual Auto-Regression) has demonstrated that a coarse-to-fine “next-scale prediction” can significantly improve generation quality, inference speed, and generalization. Because this coarse-to-fine approach closely aligns with the progressive nature of filling missing geometry in point clouds, we were inspired to develop PARC (Patch-Aware Coarse-to-Fine Refinement Completion), a two-stage multimodal framework specifically designed for handling missing structures. In the pretraining stage, PARC leverages complete point clouds alongside a Patch-Aware Coarse-to-Fine Refinement (PAR) strategy and a Mixture-of-Experts (MoE) architecture to generate high-quality local fragments, thereby improving geometric structure understanding and feature representation quality. During finetuning, the model is adapted to partial scans, further enhancing its resilience to incomplete inputs. To address remaining uncertainties in areas with missing structure, we introduce a dual-branch architecture that incorporates image cues: point cloud and image features are extracted independently and then fused via the MoE with an alignment loss, allowing complementary modalities to guide reconstruction in occluded or missing regions. Experiments conducted on the ShapeNet-ViPC dataset show that PARC has achieved highly competitive performance. Code is available at https://github.com/caiyujiaocyj/PARC.

Reducing the triangle count in complex 3D models is a basic geometry preprocessing step in graphics pipelines such as efficient rendering and interactive editing. However, most existing mesh simplification methods exhibit a few issues. Firstly, they often lead to self-intersections during decimation, a major issue for applications such as 3D printing and soft-body simulation. Second, to perform simplification on a mesh in the wild, one would first need to perform re-meshing, which often suffers from surface shifts and losses of sharp features. Finally, existing re-meshing and simplification methods can take minutes when processing large-scale meshes, limiting their applications in practice. To address the challenges, we introduce a novel GPU-based mesh optimization approach containing three key components: (1) a parallel re-meshing algorithm to turn meshes in the wild into watertight, manifold, and intersection-free ones, and reduce the prevalence of poorly shaped triangles; (2) a robust parallel simplification algorithm with intersection-free guarantees; (3) an optimization-based safe projection algorithm to realign the simplified mesh with the input, eliminating the surface shift introduced by re-meshing and recovering the original sharp features. The algorithm demonstrates remarkable efficiency, simplifying a 2-million-face mesh to 20k triangles in 3 seconds on RTX4090. We evaluated the approach on the Thingi10K dataset and showcased its exceptional performance in geometry preservation and speed. https://seonghunn.github.io/pamo/