# Shallow2Deep: Indoor Scene Modeling by Single Image Understanding

arXiv: https://arxiv.org/abs/2002.09790 · HTML: https://mlsanity.com/paper/2002.09790/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [LAYOUTDREAMER: Physics-guided Layout for Text-to-3D Compositional Scene Generation](https://mlsanity.com/paper/2502.01949/) (2025)
- [Uncertainty quantification in automated valuation models with spatially weighted conformal prediction](https://mlsanity.com/paper/2312.06531/) (2023)
- [Atlanta Scaled layouts from non-central panoramas](https://mlsanity.com/paper/2401.17058/) (2024)
- [Visual Question Answering From Another Perspective: CLEVR Mental Rotation Tests](https://mlsanity.com/paper/2212.01639/) (2022)

## Abstract (arXiv)

Dense indoor scene modeling from 2D images has been bottlenecked due to the absence of depth information and cluttered occlusions. We present an automatic indoor scene modeling approach using deep features from neural networks. Given a single RGB image, our method simultaneously recovers semantic contents, 3D geometry and object relationship by reasoning indoor environment context. Particularly, we design a shallow-to-deep architecture on the basis of convolutional networks for semantic scene understanding and modeling. It involves multi-level convolutional networks to parse indoor semantics/geometry into non-relational and relational knowledge. Non-relational knowledge extracted from shallow-end networks (e.g. room layout, object geometry) is fed forward into deeper levels to parse relational semantics (e.g. support relationship). A Relation Network is proposed to infer the support relationship between objects. All the structured semantics and geometry above are assembled to guide a global optimization for 3D scene modeling. Qualitative and quantitative analysis demonstrates the feasibility of our method in understanding and modeling semantics-enriched indoor scenes by evaluating the performance of reconstruction accuracy, computation performance and scene complexity.