← Back to all news
arXiv AI By Ziang Zhang, Wenyou Yi, Zehan Wang, Jiachen Xu, Xintong Han, Chunchao Guo, Zhou Zhao

Easy-3Dfy: Object-Level Geometric Composition for Single-Image 3D Scene Construction

Read the original on arXiv AI →

Easy-3Dfy introduces a compositional workflow for single-image 3D scene construction that separates object generation from scene layout estimation. By using a specialized 3D object generator for detailed geometry and a 3D foundation model for spatial understanding, the method aligns object-local meshes with partial scene pointmaps via an RPF-based aligner. This approach consistently outperforms SAM-3Dfy on geometric metrics in Scene-Maker and 3D-FUTURE200 without requiring full scene-level supervision.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computer Vision
Sep 23

Fysiverse-3D-Vision Technical Report: Generating Executable 3D Worlds from Images through Unified Spatial Reasoning

Fysiverse-3D-Vision is a unified vision‑language‑geometry framework that reconstructs executable 3D scenes from a single image. It separates spatial layout reasoning from asset synthesis, using a shared representation where spatial reasoning and geometric reconstruction reinforce each other. The model employs a Transformer that integrates textual supervision, semantic visual cues, and geometric representations, and includes an object‑conditioned layout module to predict object translation, rotation, and scale while maintaining physical consistency through collision‑aware optimization.

By Dingkang Yang, Yizhou Liu, Wendong Cheng, Zizhi Chen, Shunli Wang, Yang Liu, Hongsheng Li, Lihua Zhang
llmsroboticsmultimodalsafety
More like this →