arXiv Computer Vision

P-CORE: Self-Supervised Surface Consistency for Point-Based Neural Editing

P-CORE introduces a self‑supervised surface consistency technique for point‑based neural representations, enabling robust adaptation to large deformations without needing ground‑truth deformed images. By generating random deformations and enforcing that the predicted surface after deformation matches the deformation applied to the original surface prediction, the method leverages attention‑based point representations with a learned interpolation kernel. Experiments on synthetic benchmarks and real‑world datasets show improved zero‑shot editing performance and reduced artifacts compared to existing point‑based approaches.

arXiv AI
Jul 28

DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfaces

arXiv:2603. 05607v2 Announce Type: replace-cross Abstract: Computer-Aided Design (CAD) relies on structured and editable geometric representations, yet existing generative methods are constrained by small annotated datasets with explicit design histories or boundary representation (BRep) labels.

By Mohammad Sadil Khan, Muhammad Usama, Rolandos Alexandros Potamias, Didier Stricker, Muhammad Zeshan Afzal, Jiankang Deng, Ismail Elezi
Hugging Face Trending Papers
Sep 3

PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations

PointGT is a point-based 3D representation that allows simultaneous editing of both object geometry and appearance. Unlike volumetric 3D representations, which are hard to edit, PointGT combines a geometry-friendly point structure with a learned UV mapping for high-resolution texture editing. The method demonstrates fine-grained edits to geometry and texture while maintaining high rendering quality.

arXiv Computer Vision
Sep 4

PointGT: Simultaneous Geometry and Texture Editing for Point-Based Representations

PointGT is a point-based 3D representation that allows simultaneous editing of both geometry and texture. It combines a point-based structure, which is well-suited for geometry deformations, with a learned UV mapping technique that supports high-resolution texture editing. The method demonstrates fine-grained editing capabilities while maintaining high rendering quality.

By Yanshu Zhang, George Shramko, Pratul P. Srinivasan, Ke Li
arXiv Computer Vision
Sep 3

Geometry-Guided Modeling of Foundation Features Enables Generalizable Object Shape Deformation Learning

The paper introduces a generalizable deformation learning framework that reconstructs 3D objects by deforming a category-level shape template to match a monocular observation. It employs a geometry-guided feature modeling mechanism to enrich foundation features with template topology, creating a geometry-aware representation that is explicitly correlated with the target observation for precise deformation. A view-adaptive feature aggregation module further bridges the gap between the fixed template and arbitrary target views by leveraging multi-view template features and camera poses, ensuring robust feature alignment across diverse viewpoints.

By Yiyao Ma, Kai Chen, Zhongxiang Zhou, Zhuheng Song, Dongsheng Xie, Zelong Tan, Rong Xiong, Qi Dou