The paper introduces SceneEvolve, a framework that represents 3D scenes as executable code and improves reconstruction by evolving a harness around a small open‑source agent. SceneEvolve combines a compositional semantic action interface (SceneAct) with a harness evolution process (SceneHarness‑RSI) that uses a stronger teacher agent for iterative refinement. The resulting harness allows the small Qwen3.8‑27B model to reconstruct unseen scenes without parameter updates, achieving higher reconstruction quality and lower inference time than larger proprietary models across multiple datasets.
arXiv:2609.36380v1 Announce Type: new Abstract: A 3D scene reconstructed from a single image is most useful when represented not as a rendering or a fixed 3D output, but as an explicit scene program...
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.
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.
arXiv:2604.19907v2 Announce Type: replace Abstract: Recent agentic frameworks for 3D scene synthesis have advanced realism and diversity by integrating heterogeneous generation and editing tools. The...
GRASE is a new agentic framework for converting real-world video into editable 3D scenes while preserving geometry and layout. It treats captured multi‑view video as a persistent reference, revising intermediate hypotheses whenever rendered evidence conflicts with the capture. By iteratively refining architectural shells, asset placement, and geometry with cross‑view feedback, GRASE produces faithful, consistent scenes that outperform existing state‑of‑the‑art methods.
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...
SceneAgent is an agentic pipeline that transforms 3D and 4D visual captures—such as 3D Gaussian Splatting, photogrammetry, LiDAR, AI‑generated scenes, and dynamic 4D videos—into interactive simulation environments. It coordinates object reconstruction, physical modeling, part decomposition, articulation, and deformable simulation, and then generates diverse object variants and scripted demonstrations to support policy learning. In benchmark tests, SceneAgent outperforms SimFoundry on cluttered bin scenes and dramatically improves dynamic IoU on 4DCodeBench, while also enabling simulated interactions and preliminary real‑robot policy transfer.
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.
arXiv:2610.16146v1 Announce Type: cross Abstract: We present Unravel, a training-free framework that turns a single image into an interactive 3D scene, where every object is an independent and movabl...
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...
arXiv:2610.28585v1 Announce Type: cross Abstract: Simulation is a powerful tool for robotics, but building diverse, simulation-ready scenes at scale remains difficult. Reconstructing scenes from sing...
CADFather is an autonomous system that reconstructs editable CAD models from 3D meshes by coordinating vision‑language assistants, learned and algorithmic tools, and numerical optimization. It evaluates and refines candidate CAD programs, maintaining alternatives for each part and preserving the best valid result throughout reconstruction. The system uses pretrained models without additional training and is evaluated on multiple benchmark datasets for reconstruction quality and execution validity.