# DeBaRA: Denoising-Based 3D Room Arrangement Generation arXiv: https://arxiv.org/abs/2409.18336 ยท HTML: https://mlsanity.com/paper/2409.18336/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. - Builds on 60 of its 67 references that are in the map. - First posted on arXiv 26 Sep 2024. ## Placed in - Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/) ## Builds on (most cited first, 30 of 60) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021) - [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015) - [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016) - [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://mlsanity.com/paper/2112.10741/) (2021) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022) - [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018) - [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018) - [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021) - [On Distillation of Guided Diffusion Models](https://mlsanity.com/paper/2210.03142/) (2022) - [Maximum Likelihood Training of Score-Based Diffusion Models](https://mlsanity.com/paper/2101.09258/) (2021) - [Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2011.02523/) (2020) - [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021) - [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022) - [Analyzing and Improving the Training Dynamics of Diffusion Models](https://mlsanity.com/paper/2312.02696/) (2023) - [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020) - [Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model](https://mlsanity.com/paper/2311.06214/) (2023) ## Built on by (most cited first, 20 of 20) - [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025) - [CasaGPT: Cuboid Arrangement and Scene Assembly for Interior Design](https://mlsanity.com/paper/2504.19478/) (2025) - [FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts](https://mlsanity.com/paper/2506.02781/) (2025) - [ReSpace: Text-Driven Autoregressive 3D Indoor Scene Synthesis and Editing](https://mlsanity.com/paper/2506.02459/) (2025) - [LACONIC: A 3D Layout Adapter for Controllable Image Creation](https://mlsanity.com/paper/2507.03257/) (2025) - [SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis](https://mlsanity.com/paper/2508.18597/) (2025) - [I-Scene: 3D Instance Models are Implicit Generalizable Spatial Learners](https://mlsanity.com/paper/2512.13683/) (2025) - [HLG: Comprehensive 3D Room Construction via Hierarchical Layout Generation](https://mlsanity.com/paper/2508.17832/) (2025) - [Constructing a 3D Scene from a Single Image](https://mlsanity.com/paper/2505.15765/) (2025) - [Self-Evolving 3D Scene Generation from a Single Image](https://mlsanity.com/paper/2512.08905/) (2025) - [RoomPlanner: Explicit Layout Planner for Easier LLM-Driven 3D Room Generation](https://mlsanity.com/paper/2511.17048/) (2025) - [SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis](https://mlsanity.com/paper/2601.07218/) (2026) - [CasLayout: Cascaded 3D Layout Diffusion for Indoor Scene Synthesis with Implicit Relation Modeling](https://mlsanity.com/paper/2604.27361/) (2026) - [HierRelTriple: Guiding Indoor Layout Generation with Hierarchical Relationship Triplet Losses](https://mlsanity.com/paper/2503.20289/) (2025) - [SceneTeract: Probing and Improving Agent-Aware Activity Reasoning in 3D Indoor Scenes](https://mlsanity.com/paper/2603.29798/) (2026) - [Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments](https://mlsanity.com/paper/2607.02407/) (2026) - [PolyLayout: Hierarchical VLM-Guided Layout Generation Beyond Rectangular Rooms](https://mlsanity.com/paper/2608.10838/) (2026) - [PoseAdapter: Dual-Stream 2.5D Controllable Image Generation for Complex Multi-Object Scenes](https://mlsanity.com/paper/2608.15583/) (2026) - [ScenePilot: Grow-and-Repair Policy for Text-Driven 3D Indoor Scene Generation](https://mlsanity.com/paper/2608.30307/) (2026) - [Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction](https://mlsanity.com/paper/2609.23796/) (2026) ## Abstract (arXiv) 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.