arXiv:2606. 24206v1 Announce Type: cross Abstract: Recent breakthroughs in 3D generation have advanced notably with the development of text-to-image diffusion model.
By Chang Liu, Mingwen Shao, Xiang Lv, Xinyuan Chen, Lingzhuang Meng, Qiao Zhang, Zhengyi Gong, Jinghao Hu
CADSplat is a framework that reconstructs photorealistic, geometrically accurate digital twins from fewer than 15 wide‑baseline images by regularizing 3D Gaussian Splatting with an explicit CAD shape prior. It matches segmented object silhouettes to a CAD library to retrieve a suitable model and camera poses, then anchors Gaussian primitives to the model’s surface and jointly optimizes splat parameters, registration, and a non‑rigid deformation field. Experiments on two real‑world datasets show CADSplat outperforms baselines, especially in sparse and self‑occluded scenarios, and its gains mainly stem from constraining splats to a surface rather than the CAD shape itself.
By Kristof Overdulve, Lode Jorissen, Nick Michiels
arXiv:2605.05155v4 Announce Type: replace-cross
Abstract: As 3D Gaussian Splatting (3DGS) gains attention in immersive media and digital content creation, assessing the aesthetics of 3D scenes become...
By Chuanzhi Xu, Boyu Wei, Haoxian Zhou, Xuanhua Yin, Zihan Deng, Haodong Chen, Qiang Qu, Weidong Cai
The paper presents a method for reconstructing 3D wireframes from single 2D line drawings by treating the task as conditional depth estimation. It uses a Latent Diffusion Model with spatial conditioning on the sketch and optional partial-depth cues to predict depth for each drawn pixel, producing a 3D wireframe that faithfully matches the visible strokes. Trained on over three million synthetic image‑depth pairs from CAD wireframes, the approach achieves a best‑of‑five normalized depth error of 3.9% and demonstrates robust performance across varying shape complexities.
By Elton Cao, Hod Lipson
The paper presents a method for generating 3D staging—human poses, lighting, and camera setup—directly from affective textual descriptions. It builds a dataset of 11,911 text–staging pairs derived from 2,328 figurative paintings, reconstructing SMPL bodies, estimating illumination, and recovering camera parameters. A flow‑matching transformer is trained to produce variable‑size scenes and multiple staging alternatives, achieving a 32.2% retrieval R@1 on held‑out prompts, outperforming a CLIP‑based baseline.
By Yunge Wen
WorldSculpt presents a method for generating compositional 3D representations of cluttered scenes with hundreds of objects by adapting a single-object 3D generative prior to multi-view observations. The approach, built on Pixal3D with a multi-view conditioning pathway, can generalize to highly occluded scenes without scene-level training. The authors also introduce the UE-MeshyScene benchmark and demonstrate that their method outperforms prior approaches across various evaluation settings, including converting existing 3DGS worlds into compositional mesh scenes.
By Muyao Niu, Jixuan He, Ruihan Yu, Lian Fu, Yonghao Yu, Zheng-Hui Huang, Yifan Zhan, Fengbo Lan, Yongtao Ge, Yinqiang Zheng, Kaipeng Zhang, Zhixiang Wang