DistScene: Object-to-Scene Distillation for 3D Scene Generation
Not on the map. Why?
Abstract (arXiv)
We present DistScene, a framework for single-image compositional 3D scene generation by jointly modeling the environment and individual objects. Unlike existing methods that represent scenes primarily as collections of objects, we model the environment as an explicit scene component to provide geometric context for object placement. Specifically, we introduce Scene-Frame Generation, which jointly generates separate environment and object components in a shared coordinate frame, allowing their geometry and relative placement to be learned together. Then we introduce Object-Centric Refinement to refine each object in a local frame with scene context. Finally, we develop Object-to-Scene Distillation to transfer pretrained object-generation priors to scene generation through automatically composed and rendered synthetic scenes. Evaluations on indoor and outdoor benchmarks demonstrate improved scene-level spatial coherence over the evaluated baselines. Project page: https://coolbeam.github.io/DistScene/
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 46 of its 51 references that are in the map. First posted on arXiv 3 Oct 2026.
Builds on
46 of 51 references.
References · 46
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
- 2023DUSt3R: Geometric 3D Vision Made Easycited by 1,567
- 2025VGGT: Visual Geometry Grounded Transformercited by 1,539
- 2024Depth Anything V2cited by 1,422
- 2024Depth Anything: Unleashing the Power of Large-Scale Unlabeled Datacited by 1,360
- 2024Grounded SAM: Assembling Open-World Models for Diverse Visual Taskscited by 929
- 2025SAM 3: Segment Anything with Conceptscited by 906
- 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 770
- 2025Depth Anything 3: Recovering the Visual Space from Any Viewscited by 692
- 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 455
- 20203D-FUTURE: 3D Furniture shape with TextUREcited by 311
- 2024MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervisioncited by 299
34 more
- 2025MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Detailscited by 261
- 2025SAM 3D: 3Dfy Anything in Imagescited by 247
- 2025TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Modelscited by 213
- 2025Native and Compact Structured Latents for 3D Generationcited by 175
- 2020Total3DUnderstanding: Joint Layout, Object Pose and Mesh Reconstruction for Indoor Scenes from a Single Imagecited by 172
- 2025Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Materialcited by 165
- 2025Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Detailscited by 125
- 2024CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refinercited by 123
- 2024MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generationcited by 117
- 2025Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attentioncited by 110
- 2025CAST: Component-Aligned 3D Scene Reconstruction from an RGB Imagecited by 106
- 2025Hi3DGen: High-fidelity 3D Geometry Generation from Images via Normal Bridgingcited by 99
- 2025Sparc3D: Sparse Representation and Construction for High-Resolution 3D Shapes Modelingcited by 89
- 2025PartCrafter: Structured 3D Mesh Generation via Compositional Latent Diffusion Transformerscited by 84
- 2025SparseFlex: High-Resolution and Arbitrary-Topology 3D Shape Modelingcited by 79
- 2025SpatialLM: Training Large Language Models for Structured Indoor Modelingcited by 65
- 2024Gen3DSR: Generalizable 3D Scene Reconstruction via Divide and Conquer from a Single Viewcited by 61
- 2024GRUtopia: Dream General Robots in a City at Scalecited by 59
- 2024InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Modelscited by 56
- 2025SceneGen: Single-Image 3D Scene Generation in One Feedforward Passcited by 54
- 2025Efficiently Reconstructing Dynamic Scenes One D4RT at a Timecited by 54
- 2025Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformerscited by 51
- 2024Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusioncited by 50
- 2022Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenescited by 44
- 2024Zero-Shot Scene Reconstruction from Single Images with Deep Prior Assemblycited by 35
- 2026VGGT-Omegacited by 26
- 2025SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Modelcited by 17
- 2025GeoSAM2: Unleashing the Power of SAM2 for 3D Part Segmentationcited by 13
- 2024DetailGen3D: Generative 3D Geometry Enhancement via Data-Dependent Flowcited by 11
- 20263D-Fixer: Coarse-to-Fine In-place Completion for 3D Scenes from a Single Imagecited by 11
- 2026Boxer: Robust Lifting of Open-World 2D Bounding Boxes to 3Dcited by 7
- 2025ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editingcited by 5
- 2026AutoWeather4D: Autonomous Driving Video Weather Conversion via G-Buffer Dual-Pass Editingcited by 4
- 2025PoseMaster: A Unified 3D Native Framework for Stylized Pose Generationcited by 3
Built on by
Not cited yet.