DeBaRA: Denoising-Based 3D Room Arrangement Generation

• URL: http://arxiv.org/abs/2409.18336v2
• Date: Tue, 5 Nov 2024 16:30:30 GMT
• Title: DeBaRA: Denoising-Based 3D Room Arrangement Generation
• Authors: Léopold Maillard, Nicolas Sereyjol-Garros, Tom Durand, Maks Ovsjanikov,
• Abstract summary: We introduce DeBaRA, a score-based model specifically tailored for precise, controllable and flexible arrangement generation in a bounded environment. We demonstrate that by focusing on spatial attributes of objects, a single trained DeBaRA model can be leveraged at test time to perform several downstream applications such as scene synthesis, completion and re-arrangement.
• Score: 22.96293773013579
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Generating realistic and diverse layouts of furnished indoor 3D scenes unlocks multiple interactive applications impacting a wide range of industries. The inherent complexity of object interactions, the limited amount of available data and the requirement to fulfill spatial constraints all make generative modeling for 3D scene synthesis and arrangement challenging. Current methods address these challenges autoregressively or by using off-the-shelf diffusion objectives by simultaneously predicting all attributes without 3D reasoning considerations. In this paper, we introduce DeBaRA, a score-based model specifically tailored for precise, controllable and flexible arrangement generation in a bounded environment. We argue that the most critical component of a scene synthesis system is to accurately establish the size and position of various objects within a restricted area. Based on this insight, we propose a lightweight conditional score-based model designed with 3D spatial awareness at its core. We demonstrate that by focusing on spatial attributes of objects, a single trained DeBaRA model can be leveraged at test time to perform several downstream applications such as scene synthesis, completion and re-arrangement. Further, we introduce a novel Self Score Evaluation procedure so it can be optimally employed alongside external LLM models. We evaluate our approach through extensive experiments and demonstrate significant improvement upon state-of-the-art approaches in a range of scenarios.

Related papers

• MObI: Multimodal Object Inpainting Using Diffusion Models [52.07640413626605]
  This paper introduces MObI, a novel framework for Multimodal Object Inpainting. Using a single reference RGB image, MObI enables objects to be seamlessly inserted into existing multimodal scenes. Unlike traditional inpainting methods that rely solely on edit masks, our 3D bounding box conditioning gives objects accurate spatial positioning and realistic scaling.
  arXiv  Detail & Related papers  (2025-01-06T17:43:26Z)
• ObjVariantEnsemble: Advancing Point Cloud LLM Evaluation in Challenging Scenes with Subtly Distinguished Objects [1.5408065585641535]
  3D scene understanding is an important task, and there has been a recent surge of research interest in aligning 3D representations of point clouds with text to empower embodied AI. Due to the lack of comprehensive 3D benchmarks, the capabilities of 3D models in real-world scenes, particularly those that are challenging with subtly distinguished objects, remain insufficiently investigated.
  arXiv  Detail & Related papers  (2024-12-19T13:27:58Z)
• MOVIS: Enhancing Multi-Object Novel View Synthesis for Indoor Scenes [35.16430027877207]
  MOVIS aims to enhance the structural awareness of the view-conditioned diffusion model for multi-object NVS. We introduce an auxiliary task requiring the model to simultaneously predict novel view object masks. To evaluate the plausibility of synthesized images, we propose to assess cross-view consistency and novel view object placement.
  arXiv  Detail & Related papers  (2024-12-16T05:23:45Z)
• Mixed Diffusion for 3D Indoor Scene Synthesis [55.94569112629208]
  We present MiDiffusion, a novel mixed discrete-continuous diffusion model designed to synthesize plausible 3D indoor scenes. We show it outperforms state-of-the-art autoregressive and diffusion models in floor-conditioned 3D scene synthesis.
  arXiv  Detail & Related papers  (2024-05-31T17:54:52Z)
• Zero123-6D: Zero-shot Novel View Synthesis for RGB Category-level 6D Pose Estimation [66.3814684757376]
  This work presents Zero123-6D, the first work to demonstrate the utility of Diffusion Model-based novel-view-synthesizers in enhancing RGB 6D pose estimation at category-level. The outlined method shows reduction in data requirements, removal of the necessity of depth information in zero-shot category-level 6D pose estimation task, and increased performance, quantitatively demonstrated through experiments on the CO3D dataset.
  arXiv  Detail & Related papers  (2024-03-21T10:38:18Z)
• LiveHPS: LiDAR-based Scene-level Human Pose and Shape Estimation in Free Environment [59.320414108383055]
  We present LiveHPS, a novel single-LiDAR-based approach for scene-level human pose and shape estimation. We propose a huge human motion dataset, named FreeMotion, which is collected in various scenarios with diverse human poses.
  arXiv  Detail & Related papers  (2024-02-27T03:08:44Z)
• Stereo Neural Vernier Caliper [57.187088191829886]
  We propose a new object-centric framework for learning-based stereo 3D object detection. We tackle a problem of how to predict a refined update given an initial 3D cuboid guess. Our approach achieves state-of-the-art performance on the KITTI benchmark.
  arXiv  Detail & Related papers  (2022-03-21T14:36:07Z)
• ATISS: Autoregressive Transformers for Indoor Scene Synthesis [112.63708524926689]
  We present ATISS, a novel autoregressive transformer architecture for creating synthetic indoor environments. We argue that this formulation is more natural, as it makes ATISS generally useful beyond fully automatic room layout synthesis. Our model is trained end-to-end as an autoregressive generative model using only labeled 3D bounding boxes as supervision.
  arXiv  Detail & Related papers  (2021-10-07T17:58:05Z)

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.