arXiv Computer Vision
RDT-3D: Resolution-Decoupled Training for High-Fidelity 3D Geometry Generation
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
2d ago
RDT-3D: Resolution-Decoupled Training for High-Fidelity 3D Geometry Generation
High-fidelity 3D geometry generation requires maintaining global structural coherence alongside fine-grained surface details. While recent coarse-to-fine frameworks leverage Rotary Position Embeddings...
arXiv Computer Vision
4d ago
VoxLution: Evolving Voxels for High-Fidelity 3D Scene Generation
VoxLution is a coarse‑to‑fine transformer‑based method for generating high‑fidelity 3D indoor scenes. It first creates a low‑resolution spatial layout and then progressively refines the geometry in local chunks using a sequence‑modeling approach over multi‑resolution sparse voxel grids. The method introduces a resolution‑invariant tokenization scheme that encodes octants into a single decimal number and incorporates adjacent chunk boundaries to maintain coherence, achieving state‑of‑the‑art performance on 3D‑FRONT and ScanNet++ benchmarks.
arXiv AI
Oct 9
HI3D 3.0 (Twinkle3D): Object-specific 3D Asset Generation with High Resolution
Hi3D 3.0, also called Twinkle3D, is an image‑to‑3D generation system that produces watertight triangle meshes at a resolution of 2048³. It improves on previous methods by maintaining high representational precision while fixing surface quality and non‑watertight issues, scaling diffusion generation to 300K geometric tokens, strengthening global shape and local detail in a single stage, and adding a fine‑grained image‑3D cross‑modal interaction to better recover object‑specific structures such as inscriptions. The system outperforms four commercial competitors, recovering 82.1 % of inscribed characters with 98.2 % precision versus 21.7 % recall for the best rival.
arXiv Computer Vision
Sep 22
ScaleBlind: Point Cloud Completion under Unknown Scale
arXiv:2609.23404v1 Announce Type: new Abstract: Point cloud completion aims to infer a complete 3D shape from a partial point cloud and serves as a fundamental building block for downstream tasks suc...
arXiv Computer Vision
Sep 23
Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction
arXiv:2609.23796v2 Announce Type: replace Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open ch...
arXiv Computer Vision
Sep 16
Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
arXiv:2511.21265v2 Announce Type: replace Abstract: Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) en...