EgoSurg is a framework that reconstructs dynamic operating room scenes from sparse wall‑mounted stereo video and renders arbitrary, role‑specific egocentric views without instrumenting personnel. It builds a per‑timestamp 3D Gaussian Splatting representation using scale‑aware stereo depth and refines it with an image‑conditioned diffusion model to correct artifacts from limited camera coverage, crowding, and occlusion. Evaluations on real robotic pulmonology procedures and simulated sessions show consistent near‑field reconstruction fidelity (PSNR 26.8 dB, SSIM 0.895) and synthesized egocentric view quality (PSNR 17.8 dB, SSIM 0.766) across workflow phases and hospital sites, with case studies demonstrating applications such as sterile field violation adjudication, role‑specific replay, and counterfactual personnel positioning.
By Han Zhang, Lalithkumar Seenivasan, Jose L. Porras, Roger D. Soberanis-Mukul, Hao Ding, Hongchao Shu, Benjamin D. Killeen, Ankita Ghosh, Lonny Yarmus, Jeffrey K. Jopling, Masaru Ishii, Angela C. Argento, Mathias Unberath
arXiv:2610.01863v1 Announce Type: new
Abstract: We present LiteReality-Agent, an agentic system for reconstructing real indoor environments as realistic, articulated, and simulation-ready 3D scenes f...
By Zhening Huang, Yueyan Li, Johnathan Chiu, Xiaoyang Lyu, Matt Zhou, Yuxin Yao, Joan Lasenby, Shangzhe Wu
arXiv:2608.30821v1 Announce Type: cross
Abstract: Composable scene modeling aims to recover a real indoor scene as complete, editable object assets arranged as observed, giving robot simulation and e...
By Minghan Qin, Yuang Wang, Xiuyu Yang, Yushi Long, Yujian Zhang, Ruihuan Wang, Kai Ye, Yangang Zhang, Hang Li
PlanCraft introduces a progressive approach to 3D residential scene generation that mirrors how architects design: starting with rough sketches and refining them over time. It leverages a large dataset of real floor plans to train a SketchPlan module that generates partial sketches at various completion levels, a PlanCraft‑Diff module that sharpens these sketches into precise vector floor plans, and a PlanCraft‑Agent that furnishes rooms within the established spatial contract. The method outperforms existing 2D and 3D baselines, achieving a 61.1% lower FID and a 15‑point lead in expert‑rated spatial rationality, even with only 25% sketch completion.
By Pengyu Zeng, Yuqin Dai, Jun Yin, Ziyang Han, Ng Cheuk Hei, Jing Zhong, Chaoyang Shi, ZhanXiang Jin, Maowei Jiang, Shuai Lu
MEGA is a new framework that extracts object-level, watertight meshes from 3D Gaussian Splatting (3DGS) scenes. It uses a segment-then-mesh approach, leveraging Spatial Visual Distillation (SVD) to sample diverse camera views of each segmented object and train a mesh reconstruction model with photometric supervision. Experiments on popular benchmarks show that MEGA outperforms existing methods in accurately recovering object-level 3D occupancy and supports complex physical interactions by combining high-quality meshes with photorealistic 3DGS rendering.
By Liwei Liao, Yingkui Zhang, Qianqian Tong, Ronggang Wang
arXiv:2605. 10873v2 Announce Type: replace-cross Abstract: Recovering editable CAD programs from images or 3D observations is central to AI-assisted design, but progress is difficult to measure because existing evaluations are fragmented across datasets, modalities, and metrics.
By Anna C. Doris, Jacob Thomas Sony, Ghadi Nehme, Era Syla, Amin Heyrani Nobari, Faez Ahmed