Indoor Scene Understanding and Layout Estimation
Summary
Indoor scene understanding encompasses the extraction of both semantic and geometric information from images or sensor data to interpret the structure and contents of enclosed environments. Layout estimation forms a core component by predicting the spatial configuration of walls, ceilings and floors, often under the Manhattan world assumption that indoor scenes comprise orthogonal planes aligned with three dominant orientations. Early methods relied on hand-crafted features and vanishing-point detection to generate coarse cuboid models from single or multiple images. Recent progress has been driven by deep learning architectures that integrate multi-scale feature representations, global context modelling and end-to-end training to recover complex room geometries from single-view or panoramic inputs. These advances not only improve accuracy in cluttered or non-cuboid layouts but also enable real-time performance for applications in robotics navigation, augmented reality visualisation and energy-efficient building management. By combining geometric priors with data-driven segmentation and keypoint regression, current systems achieve robust scene completion, semantic labelling and 3D reconstruction even in occluded or partially scanned environments.
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Indoor Scene Understanding and Layout Estimation publication trend
The graph below shows the total number of articles in indoor scene understanding and layout estimation across all publications each year (not limited to Nature Index journals).
Technical terms
Manhattan world assumption: The hypothesis that indoor scenes consist of three orthogonal sets of planes aligned with dominant room axes.
Vanishing point: A projection where parallel lines in 3D space appear to converge in a 2D image, used to infer scene orientation.
Feature Pyramid Network (FPN): A convolutional architecture that merges multi-scale feature maps to enhance localisation and segmentation tasks.
Semantic segmentation: The pixel-wise classification of an image into predefined categories, such as walls, floor and furniture.
Panoramic image: An image capturing a wide or full 360° field of view, typically represented in equirectangular projection for scene analysis.
References
- MreNet: A Vision Transformer Network for Estimating Room Layouts from a Single RGB Panorama. Applied Sciences (2022).
- Toward Enhancing Room Layout Estimation by Feature Pyramid Networks. Data Science and Engineering (2022).
- 3D layout estimation of general rooms based on ordinal semantic segmentation. IET Computer Vision (2023).
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