HARMONY is a hierarchical chain-of-thought framework that reconstructs complete 3D indoor scenes from a single monocular image. It combines agentic reasoning with visual geometry foundation models, starting with camera calibration and semantic layout recovery, then placing objects hierarchically while refining geometry with point cloud estimations. The method achieves semantically consistent scenes that align perceptually with the input image, outperforming existing baselines on synthetic and real-world data.
By Shufan Sun, Chen Wang, Enxin Song, Jiatao Gu, Lingjie Liu
arXiv:2606. 27923v1 Announce Type: cross Abstract: We present Home3D 1.
By Yiyun Fei, Guoqiu Li, Jin Song, Chuqiao Wu, Delong Wu, Hong Wu, Ziru Zeng, Haohui Chen, YinDong Kong, Jing Li, Qi Wu, Feng Zhang
SceneBloom is an agentic framework that generates realistic, varied, and simulation-ready indoor environments for embodied learning. It combines diffusion-based visual layout sampling with a contract-guided 3D compiler that uses extracted relations for geometric fitting and pose correction. The method outperforms baselines across five room types, achieving higher visual quality, physical stability, and better alignment with real room distributions, and improves object navigation policy success rates on real scans.
By Yuhan Liu, Lianhui Qin, Shenji Wan
arXiv:2605.15843v2 Announce Type: replace
Abstract: Recent 3D world modeling systems based on generative scene synthesis, such as Marble, can create coherent and explorable 3D environments, yet their...
By Jichen Hu, Jiawei Guo, Jiazhong Cen, Chen Yang, Sikuang Li, Wei Shen
arXiv:2606. 10953v1 Announce Type: new Abstract: Furnished floor plans are fundamental to real estate visualization, interior design, and architectural workflows.
By Fedor Rodionov, Aleksandar Cvejic, Michael Birsak, John Femiani, Peter Wonka
DistScene is a framework for generating 3D scenes from a single image by jointly modeling the environment and individual objects. It introduces Scene-Frame Generation to produce separate environment and object components in a shared coordinate frame, and Object-Centric Refinement to fine‑tune each object with scene context. The method also employs Object-to-Scene Distillation to transfer pretrained object-generation priors to scene generation using automatically composed synthetic scenes, achieving improved spatial coherence on indoor and outdoor benchmarks.
By Kunming Luo, Hongyu Yan, Ken Deng, Chengcheng Zhou, Tianyu Liu, Haipeng Li, Haibin Huang, Xuelong Li, Ping Tan
arXiv:2608.30423v1 Announce Type: cross
Abstract: Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a...
By Minhas Kamal, Hiranya Garbha Kumar, Mahedi Kamal, Balakrishnan Prabhakaran
arXiv:2606. 06390v1 Announce Type: cross Abstract: Indoor scene generation is crucial for robot simulation and modern interior design.
By Wenbo Li, Xiaoliang Ju, Zipeng Qin, Rongyao Fang, Hongsheng Li
arXiv:2606.13345v2 Announce Type: replace
Abstract: Existing 3D scene editing methods typically rely on per-scene optimization over explicit 3D representations or cascaded edit-and-reconstruct pipeli...
By Xinnan Zhu, Ruijie Xu, Jiayu Ying, Daoguo Dong, Jiachen Xu, Yuan Xie, Xin Tan
arXiv:2603. 16085v2 Announce Type: replace-cross Abstract: Recent breakthroughs in 3D generation have enabled the synthesis of high-fidelity individual assets.
By Hui Shan, Keyang Luo, Ming Li, Sizhe Zheng, Yanwei Fu, Zhen Chen, Xiangru Huang
Splatting-based algorithms reconstruct photorealistic, real-time-renderable, and mesh-exportable 3D scenes from regular images, but they represent a scene as a single monolithic field. Therefore, the...
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