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
The paper introduces a fusion‑aware hierarchical Gaussian patch representation that enables direct class‑guided generation of 3D Gaussian Splatting (3DGS) objects. By decomposing irregular Gaussian sets into canonical local patches and encoding them as structured tokens, the method fuses global class semantics with patch‑level geometry, appearance, spatial correspondence, and rendering‑sensitive cues. A structure‑aware rectified flow model, conditioned on patch positions and coupled with global‑local velocity prediction and density‑aware weighting, produces class‑conditioned 3DGS objects within seconds, achieving more coherent geometry, sharper local details, and better multi‑view consistency than baseline models.
By Yizhao Wang, Jingbo Wang, Guantao Zhang
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:2605.10360v3 Announce Type: replace
Abstract: While novel view synthesis (NVS) for dynamic scenes has seen significant progress, reconstructing temporally consistent geometric surfaces remains...
By Minje Kim, Younghyun Noh, Jaesoon Kim, Tae-Kyun Kim
The paper introduces WINGS, a reference‑free Gaussian splatting inpainting technique that operates directly in 3D. It uses a large, pre‑trained 3D generative prior and a structure completion network to reconstruct missing geometry and appearance without generating reference views. The method claims faster performance and reduced multi‑view inconsistency compared to 2D diffusion‑based approaches, and its effectiveness is validated through experiments and a user study.
By No\'e Lallouet, Michael Fischer, Elie Michel
3DTexMOR introduces a method for removing multiple 3D objects from reconstructed scenes by performing texture-space inpainting rather than 2D image inpainting. The approach aggregates observations into shared texture maps, inpaints missing regions, and reprojects the completed maps to supervise Gaussian scene completion, while decomposing appearance to avoid view-dependent artifacts. Experiments show that this technique yields visually plausible results and outperforms existing methods, improving PSNR by 5.8 dB and reducing LPIPS by at least 22%.
By Kunxin Guang, Yonghao Zhao, Jian Yang, Beibei Wang