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

2024-12-23

AI Summary

Existing methods for 3D indoor scene generation often oversimplify scene structure and neglect spatial and stylistic correlations among objects, leading to distorted layouts, coarse geometry, and inconsistent appearance. To address this, we propose the Scene Implicit Neural Field (S-INF), the first framework to decouple multimodal scene relationships into two hierarchical levels—layout-level (spatial topology) and detail-level (geometry/appearance)—and jointly model them within a unified implicit neural field. S-INF introduces layout-guided implicit representation learning and layout-aware feature projection, integrated with differentiable rendering for end-to-end optimization. Experiments on 3D-FRONT demonstrate that S-INF consistently outperforms state-of-the-art methods across key metrics: layout plausibility, geometric fidelity, and cross-object style consistency. Several quantitative and qualitative results establish new state-of-the-art performance.

Technology Category

• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• 3D Indoor Scene Generation
• Learning-based Methods
• Realism and Naturalness

Innovation

• Scene Implicit Neural Fields
• Differential Rendering
• 3D Indoor Scene Synthesis