Pano3DComposer: Feed-Forward Compositional 3D Scene Generation from Single Panoramic Image

• URL: http://arxiv.org/abs/2603.05908v1
• Date: Fri, 06 Mar 2026 04:54:23 GMT
• Title: Pano3DComposer: Feed-Forward Compositional 3D Scene Generation from Single Panoramic Image
• Abstract summary: Pano3DComposer is an efficient feed-forward framework for panoramic images.<n>This module converts the 3D objects generated by off-the-shelf image-to-3D models from local to world coordinates.
• Score: 13.55946265213768
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Current compositional image-to-3D scene generation approaches construct 3D scenes by time-consuming iterative layout optimization or inflexible joint object-layout generation. Moreover, most methods rely on limited field-of-view perspective images, hindering the creation of complete 360-degree environments. To address these limitations, we design Pano3DComposer, an efficient feed-forward framework for panoramic images. To decouple object generation from layout estimation, we propose a plug-and-play Object-World Transformation Predictor. This module converts the 3D objects generated by off-the-shelf image-to-3D models from local to world coordinates. To achieve this, we adapt the VGGT architecture to Alignment-VGGT by using target object crop, multi-view object renderings and camera parameters to predict the transformation. The predictor is trained using pseudo-geometric supervision to address the shape discrepancy between generated and ground-truth objects. For input images from unseen domains, we further introduce a Coarse-to-Fine (C2F) alignment mechanism for Pano3DComposer that iteratively refines geometric consistency with feedback of scene rendering. Our method achieves superior geometric accuracy for image/text-to-3D tasks on synthetic and real-world datasets. It can generate a high-fidelity 3D scene in approximately 20 seconds on an RTX 4090 GPU. Project page: https://qiuzidian.github.io/pano3dcomposer-page/.

Related papers

• Drag4D: Align Your Motion with Text-Driven 3D Scene Generation [77.79131321983677]
  Drag4D is an interactive framework that integrates object motion control within text-driven 3D scene generation.<n>This framework enables users to define 3D trajectories for the 3D objects generated from a single image, seamlessly integrating them into a high-quality 3D background.
  arXiv  Detail & Related papers  (2025-09-26T05:23:45Z)
• Towards Geometric and Textural Consistency 3D Scene Generation via Single Image-guided Model Generation and Layout Optimization [14.673302810271219]
  We propose a novel three-stage framework for 3D scene generation with explicit geometric representations and high-quality textural details.<n>Our approach not only outperforms state-of-the-art methods in terms of geometric accuracy and texture fidelity of individual generated 3D models, but also has significant advantages in scene layout synthesis.
  arXiv  Detail & Related papers  (2025-07-20T06:59:42Z)
• Constructing a 3D Scene from a Single Image [31.11317559252235]
  SceneFuse-3D is a training-free framework designed to synthesize coherent 3D scenes from a single top-down view.<n>We decompose the input image into overlapping regions and generate each using a pretrained 3D object generator.<n>This modular design allows us to overcome resolution bottlenecks and preserve spatial structure without requiring 3D supervision or fine-tuning.
  arXiv  Detail & Related papers  (2025-05-21T17:10:47Z)
• Disentangled 3D Scene Generation with Layout Learning [109.03233745767062]
  We introduce a method to generate 3D scenes that are disentangled into their component objects. Our key insight is that objects can be discovered by finding parts of a 3D scene that, when rearranged spatially, still produce valid configurations of the same scene. We show that despite its simplicity, our approach successfully generates 3D scenes into individual objects.
  arXiv  Detail & Related papers  (2024-02-26T18:54:15Z)
• GALA3D: Towards Text-to-3D Complex Scene Generation via Layout-guided Generative Gaussian Splatting [52.150502668874495]
  We present GALA3D, generative 3D GAussians with LAyout-guided control, for effective compositional text-to-3D generation. GALA3D is a user-friendly, end-to-end framework for state-of-the-art scene-level 3D content generation and controllable editing.
  arXiv  Detail & Related papers  (2024-02-11T13:40:08Z)
• SceneWiz3D: Towards Text-guided 3D Scene Composition [134.71933134180782]
  Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. We introduce SceneWiz3D, a novel approach to synthesize high-fidelity 3D scenes from text.
  arXiv  Detail & Related papers  (2023-12-13T18:59:30Z)
• CC3D: Layout-Conditioned Generation of Compositional 3D Scenes [49.281006972028194]
  We introduce CC3D, a conditional generative model that synthesizes complex 3D scenes conditioned on 2D semantic scene layouts. Our evaluations on synthetic 3D-FRONT and real-world KITTI-360 datasets demonstrate that our model generates scenes of improved visual and geometric quality.
  arXiv  Detail & Related papers  (2023-03-21T17:59:02Z)
• DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization [66.25948693095604]
  We propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement.
  arXiv  Detail & Related papers  (2021-08-24T13:55:29Z)

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.