# S-INF: Towards Realistic Indoor Scene Synthesis via Scene Implicit Neural Field

arXiv: https://arxiv.org/abs/2412.17561 · HTML: https://mlsanity.com/paper/2412.17561/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 45 references that are in the map.
- First posted on arXiv 23 Dec 2024.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Convolutional Occupancy Networks](https://mlsanity.com/paper/2003.04618/) (2020)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics](https://mlsanity.com/paper/2011.09127/) (2020)
- [3D-FUTURE: 3D Furniture shape with TextURE](https://mlsanity.com/paper/2009.09633/) (2020)
- [ATISS: Autoregressive Transformers for Indoor Scene Synthesis](https://mlsanity.com/paper/2110.03675/) (2021)
- [SceneFormer: Indoor Scene Generation with Transformers](https://mlsanity.com/paper/2012.09793/) (2020)
- [LayoutDM: Discrete Diffusion Model for Controllable Layout Generation](https://mlsanity.com/paper/2303.08137/) (2023)
- [InstructScene: Instruction-Driven 3D Indoor Scene Synthesis with Semantic Graph Prior](https://mlsanity.com/paper/2402.04717/) (2024)
- [Human-centric Indoor Scene Synthesis Using Stochastic Grammar](https://mlsanity.com/paper/1808.08473/) (2018)
- [Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations](https://mlsanity.com/paper/2205.02825/) (2022)
- [EchoScene: Indoor Scene Generation via Information Echo over Scene Graph Diffusion](https://mlsanity.com/paper/2405.00915/) (2024)
- [Deep Generative Modeling for Scene Synthesis via Hybrid Representations](https://mlsanity.com/paper/1808.02084/) (2018)
- [Neural Fields as Learnable Kernels for 3D Reconstruction](https://mlsanity.com/paper/2111.13674/) (2021)
- [MIME: Human-Aware 3D Scene Generation](https://mlsanity.com/paper/2212.04360/) (2022)
- [SceneHGN: Hierarchical Graph Networks for 3D Indoor Scene Generation with Fine-Grained Geometry](https://mlsanity.com/paper/2302.10237/) (2023)
- [Scene Synthesis via Uncertainty-Driven Attribute Synchronization](https://mlsanity.com/paper/2108.13499/) (2021)
- [Indoor Scene Generation from a Collection of Semantic-Segmented Depth Images](https://mlsanity.com/paper/2108.09022/) (2021)
- [Learning 3D Scene Priors with 2D Supervision](https://mlsanity.com/paper/2211.14157/) (2022)
- [Pose2Room: Understanding 3D Scenes from Human Activities](https://mlsanity.com/paper/2112.03030/) (2021)
- [SceneSeer: 3D Scene Design with Natural Language](https://mlsanity.com/paper/1703.00050/) (2017)
- [RoomDesigner: Encoding Anchor-latents for Style-consistent and Shape-compatible Indoor Scene Generation](https://mlsanity.com/paper/2310.10027/) (2023)
- [External Knowledge Enhanced 3D Scene Generation from Sketch](https://mlsanity.com/paper/2403.14121/) (2024)

## Built on by (most cited first, 3 of 3)

- [SceneFoundry: Generating Interactive Infinite 3D Worlds](https://mlsanity.com/paper/2601.05810/) (2026)
- [Investigating Domain Gaps for Indoor 3D Object Detection](https://mlsanity.com/paper/2508.17439/) (2025)
- [Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation](https://mlsanity.com/paper/2607.20866/) (2026)

## Abstract (arXiv)

Learning-based methods have become increasingly popular in 3D indoor scene synthesis (ISS), showing superior performance over traditional optimization-based approaches. These learning-based methods typically model distributions on simple yet explicit scene representations using generative models. However, due to the oversimplified explicit representations that overlook detailed information and the lack of guidance from multimodal relationships within the scene, most learning-based methods struggle to generate indoor scenes with realistic object arrangements and styles. In this paper, we introduce a new method, Scene Implicit Neural Field (S-INF), for indoor scene synthesis, aiming to learn meaningful representations of multimodal relationships, to enhance the realism of indoor scene synthesis. S-INF assumes that the scene layout is often related to the object-detailed information. It disentangles the multimodal relationships into scene layout relationships and detailed object relationships, fusing them later through implicit neural fields (INFs). By learning specialized scene layout relationships and projecting them into S-INF, we achieve a realistic generation of scene layout. Additionally, S-INF captures dense and detailed object relationships through differentiable rendering, ensuring stylistic consistency across objects. Through extensive experiments on the benchmark 3D-FRONT dataset, we demonstrate that our method consistently achieves state-of-the-art performance under different types of ISS.