# Physics Inspired Optimization on Semantic Transfer Features: An Alternative Method for Room Layout Estimation

arXiv: https://arxiv.org/abs/1707.00383 · HTML: https://mlsanity.com/paper/1707.00383/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2017.

## Built on by (most cited first, 29 of 29)

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- [P-MapNet: Far-seeing Map Generator Enhanced by both SDMap and HDMap Priors](https://mlsanity.com/paper/2403.10521/) (2024)
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- [Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing](https://mlsanity.com/paper/2111.12608/) (2021)
- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection](https://mlsanity.com/paper/2304.13031/) (2023)
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- [SC-wLS: Towards Interpretable Feed-forward Camera Re-localization](https://mlsanity.com/paper/2210.12748/) (2022)
- [3D Room Layout Estimation from a Cubemap of Panorama Image via Deep Manhattan Hough Transform](https://mlsanity.com/paper/2207.09291/) (2022)
- [Semi-supervised Implicit Scene Completion from Sparse LiDAR](https://mlsanity.com/paper/2111.14798/) (2021)
- [OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas](https://mlsanity.com/paper/2104.09403/) (2021)
- [Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image](https://mlsanity.com/paper/2104.07986/) (2021)
- [Estimating Generic 3D Room Structures from 2D Annotations](https://mlsanity.com/paper/2306.09077/) (2023)
- [Polygon Detection for Room Layout Estimation using Heterogeneous Graphs and Wireframes](https://mlsanity.com/paper/2306.12203/) (2023)
- [Semantic Room Wireframe Detection from a Single View](https://mlsanity.com/paper/2206.00491/) (2022)
- [Transferable End-to-end Room Layout Estimation via Implicit Encoding](https://mlsanity.com/paper/2112.11340/) (2021)
- [DPF: Learning Dense Prediction Fields with Weak Supervision](https://mlsanity.com/paper/2303.16890/) (2023)
- [No More Ambiguity in 360deg Room Layout via Bi-Layout Estimation](https://mlsanity.com/paper/2404.09993/) (2024)
- [PAM: A Pose-Appearance-Motion Engine for Sim-to-Real HOI Video Generation](https://mlsanity.com/paper/2603.22193/) (2026)
- [Shape-Net: Room Layout Estimation from Panoramic Images Robust to Occlusion using Knowledge Distillation with 3D Shapes as Additional Inputs](https://mlsanity.com/paper/2304.12624/) (2023)
- [Seg2Reg: Differentiable 2D Segmentation to 1D Regression Rendering for 360 Room Layout Reconstruction](https://mlsanity.com/paper/2311.18695/) (2023)
- [Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors](https://mlsanity.com/paper/2412.13173/) (2024)
- [RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View](https://mlsanity.com/paper/2110.00644/) (2021)
- [ECT: Fine-grained Edge Detection with Learned Cause Tokens](https://mlsanity.com/paper/2308.03092/) (2023)
- [Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking](https://mlsanity.com/paper/2503.08370/) (2025)
- [Denoising Programming Knowledge Tracing with a Code Graph-based Tuning Adaptor](https://mlsanity.com/paper/2506.11107/) (2025)
- [Layout Anything: One Transformer for Universal Room Layout Estimation](https://mlsanity.com/paper/2512.02952/) (2025)

## Abstract (arXiv)

In this paper, we propose an alternative method to estimate room layouts of cluttered indoor scenes. This method enjoys the benefits of two novel techniques. The first one is semantic transfer (ST), which is: (1) a formulation to integrate the relationship between scene clutter and room layout into convolutional neural networks; (2) an architecture that can be end-to-end trained; (3) a practical strategy to initialize weights for very deep networks under unbalanced training data distribution. ST allows us to extract highly robust features under various circumstances, and in order to address the computation redundance hidden in these features we develop a principled and efficient inference scheme named physics inspired optimization (PIO). PIO's basic idea is to formulate some phenomena observed in ST features into mechanics concepts. Evaluations on public datasets LSUN and Hedau show that the proposed method is more accurate than state-of-the-art methods.