The paper introduces VAD, a lightweight, network‑free method for computing Unsigned Distance Fields (UDFs) from unoriented point clouds. It assigns bi‑directional normals using two Voronoi‑based criteria, diffuses these normals to approximate a UDF gradient field, and then integrates to recover the final UDF. Experiments show VAD handles watertight, open, non‑manifold, and non‑orientable geometries efficiently and stably.
arXiv:2608.15933v1 Announce Type: cross Abstract: Implicit surface representations have regained popularity because of their use in machine learning. A common component in optimization is regularizat...
Point cloud denoising is essentially a geometric recovery task that aims to reconstruct the intrinsic structure of a smooth 2D Riemannian manifold embedded in R^3 from noisy, discrete ambient-space samples. Despite the remarkable progress of modern manifold-aware encoders and generative transport models in geometric representation learning, a fundamental objective-geometry mismatch remains underexplored.
Point Diffusion Mamba (PDM) is a new method that fuses diffusion models with state‑space modeling to perform single‑view 3D reconstruction when training data are scarce. It uses a lightweight reconstruction module for unordered point‑clouds, a Local Geometric Aggregation module combined with Mamba blocks to capture both global geometry and local detail, and a Hierarchical Feature Integration Network to merge high‑level semantic and local geometric features for each point. A Dynamic Weighted Sampling strategy further improves reconstruction quality by integrating generative priors, and experiments on ShapeNet and Pix3D show that PDM outperforms existing state‑of‑the‑art approaches.
arXiv:2609.26039v1 Announce Type: cross Abstract: Point cloud generation has emerged as a crucial task for accurately capturing and reproducing the complexity of the physical world. However, existing...
PhaseSurf introduces a neural volume rendering framework that uses Metric-Phase Fields (MPFs) to handle thin structures and sign-ambiguous geometry. MPFs combine a nonnegative metric field for unsigned proximity with a continuous phase field that softly indicates sign, producing a signed composite implicit function with stable gradients. The method includes specialized training losses for metric consistency and phase transition stability, enabling robust iso-surface extraction and outperforming both SDF- and UDF-based approaches on multi-view datasets with challenging geometry.
The paper introduces a differentiable voxelization technique that computes gradients of volumetric properties, such as winding numbers, with respect to surface mesh parameters. This method allows efficient optimization of triangle meshes using voxel-based volume samples on a regular grid. The authors demonstrate its utility in applications like resolving mesh intersections, designing manufacturable shapes for bandsaw cutting, and creating near-tiling 3D structures.
arXiv:2605.10360v3 Announce Type: replace Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
arXiv:2607. 27126v1 Announce Type: new Abstract: Persistence Diagram (PD) is known to capture point cloud topology effectively, but its computation has high time complexity.
arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.
arXiv:2602. 07429v2 Announce Type: replace-cross Abstract: Boundary representation (B-rep) is the industry standard for computer-aided design (CAD).
arXiv:2606. 03260v1 Announce Type: cross Abstract: Deep learning surrogates for 3D Partial Differential Equations (PDEs) often fail to generalize across geometric transformations because they depend heavily on specific coordinate systems.
The paper presents a data‑efficient pre‑training framework for neural surrogate models solving PDEs on unstructured 3D geometries. It introduces a geometry‑driven strategy for steady‑state problems that uses intrinsic shape descriptors, and a physics‑driven approach for transient problems that generates synthetic PDE data online. Experiments show faster convergence, higher data efficiency, and improved accuracy during fine‑tuning, especially in low‑data settings.