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...
By Tobias Djuren, Markus Worchel, Ugo Finnendahl, Marc Alexa
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.
By Wei Zhou, Xinzhe Shi, Xingxing Hao, Xing Hao, Kang Li, Jinye Peng, Ying He
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...
By Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou, Athanasios Voulodimos
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.
By Junkai Deng, Jiayi Kong, Xuhui Chen, Guanli Hou, Fei Hou, Junhui Hou, Wenping Wang, Ying He
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.
By Tobias Djuren, Ugo Finnendahl, Markus Worchel, Hendrik Meyer, Marc Alexa