AnchoredDream: Zero-Shot 360° Indoor Scene Generation from a Single View via Geometric Grounding

• URL: http://arxiv.org/abs/2601.16532v2
• Date: Mon, 26 Jan 2026 07:46:19 GMT
• Title: AnchoredDream: Zero-Shot 360° Indoor Scene Generation from a Single View via Geometric Grounding
• Authors: Runmao Yao, Junsheng Zhou, Zhen Dong, Yu-Shen Liu,
• Abstract summary: Single-view indoor scene generation plays a crucial role in a range of real-world applications.<n>Recent approaches have made progress by leveraging diffusion models and depth estimation networks.<n>We propose AnchoredDream, a novel zero-shot pipeline that anchors 360 scene generation on high-fidelity geometry.
• Score: 58.90269958632018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Single-view indoor scene generation plays a crucial role in a range of real-world applications. However, generating a complete 360° scene from a single image remains a highly ill-posed and challenging problem. Recent approaches have made progress by leveraging diffusion models and depth estimation networks, yet they still struggle to maintain appearance consistency and geometric plausibility under large viewpoint changes, limiting their effectiveness in full-scene generation. To address this, we propose AnchoredDream, a novel zero-shot pipeline that anchors 360° scene generation on high-fidelity geometry via an appearance-geometry mutual boosting mechanism. Given a single-view image, our method first performs appearance-guided geometry generation to construct a reliable 3D scene layout. Then, we progressively generate the complete scene through a series of modules: warp-and-inpaint, warp-and-refine, post-optimization, and a novel Grouting Block, which ensures seamless transitions between the input view and generated regions. Extensive experiments demonstrate that AnchoredDream outperforms existing methods by a large margin in both appearance consistency and geometric plausibility--all in a zero-shot manner. Our results highlight the potential of geometric grounding for high-quality, zero-shot single-view scene generation.

Related papers

• GaMO: Geometry-aware Multi-view Diffusion Outpainting for Sparse-View 3D Reconstruction [6.362401262063673]
  GaMO is a framework that reformulates sparse-view reconstruction through multi-view outpainting.<n>Our approach employs multi-view conditioning and geometry-aware denoising strategies in a zero-shot manner without training.
  arXiv  Detail & Related papers  (2025-12-31T18:59:55Z)
• Self-Evolving 3D Scene Generation from a Single Image [44.87957263540352]
  EvoScene is a training-free framework that progressively reconstructs complete 3D scenes from single images.<n>EvoScene alternates between 2D and 3D domains, gradually improving both structure and appearance.
  arXiv  Detail & Related papers  (2025-12-09T18:44:21Z)
• GeoWorld: Unlocking the Potential of Geometry Models to Facilitate High-Fidelity 3D Scene Generation [68.02988074681427]
  Previous works leveraging video models for image-to-3D scene generation tend to suffer from geometric distortions and blurry content.<n>In this paper, we renovate the pipeline of image-to-3D scene generation by unlocking the potential of geometry models.<n>Our GeoWorld can generate high-fidelity 3D scenes from a single image and a given camera trajectory, outperforming prior methods both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2025-11-28T13:55:45Z)
• WorldGrow: Generating Infinite 3D World [75.81531067447203]
  We tackle the challenge of generating the infinitely extendable 3D world -- large, continuous environments with coherent geometry and realistic appearance.<n>We propose WorldGrow, a hierarchical framework for unbounded 3D scene synthesis.<n>Our method features three core components: (1) a data curation pipeline that extracts high-quality scene blocks for training, making the 3D structured latent representations suitable for scene generation; (2) a 3D block inpainting mechanism that enables context-aware scene extension; and (3) a coarse-to-fine generation strategy that ensures both global layout plausibility and local geometric/textural fidelity.
  arXiv  Detail & Related papers  (2025-10-24T17:39:52Z)
• G4Splat: Geometry-Guided Gaussian Splatting with Generative Prior [53.762256749551284]
  We identify accurate geometry as the fundamental prerequisite for effectively exploiting generative models to enhance 3D scene reconstruction.<n>We incorporate this geometry guidance throughout the generative pipeline to improve visibility mask estimation, guide novel view selection, and enhance multi-view consistency when inpainting with video diffusion models.<n>Our method naturally supports single-view inputs and unposed videos, with strong generalizability in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2025-10-14T03:06:28Z)
• TiP4GEN: Text to Immersive Panorama 4D Scene Generation [82.8444414014506]
  TiP4GEN is a text-to-dynamic panorama scene generation framework.<n>It enables fine-grained content control and synthesizes motion-rich, geometry-consistent panoramic 4D scenes.<n> TiP4GEN integrates panorama video generation and dynamic scene reconstruction to create 360-degree immersive virtual environments.
  arXiv  Detail & Related papers  (2025-08-17T16:02:24Z)
• HaloGS: Loose Coupling of Compact Geometry and Gaussian Splats for 3D Scenes [23.141875500453484]
  HaloGS is a dual representation that loosely couples coarse triangles for geometry with Gaussian primitives for appearance.<n>Our design yields a compact yet expressive model capable of photo realistic rendering across both indoor and outdoor environments.
  arXiv  Detail & Related papers  (2025-05-26T17:44:04Z)
• Invisible Stitch: Generating Smooth 3D Scenes with Depth Inpainting [75.7154104065613]
  We introduce a novel depth completion model, trained via teacher distillation and self-training to learn the 3D fusion process. We also introduce a new benchmarking scheme for scene generation methods that is based on ground truth geometry.
  arXiv  Detail & Related papers  (2024-04-30T17:59:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.