Source-linked AI summary
A Coarse-to-Fine Indoor Layout Estimation (CFILE) Method
Yuzhuo Ren, Chen Chen, Shangwen Li, C. -C. Jay Kuo
TL;DR
Indoor layout estimation from a single RGB image must recover occluded room boundaries in cluttered scenes. CFILE combines an MFCN coarse estimate with constraint-based optimization for fine localization, achieving state-of-the-art performance on two benchmark datasets. The method’s scope includes challenging Manhattan-layout cases and occasional errors from incorrect coarse estimates.
Problem
Indoor layout estimation must locate floor, wall, and ceiling boundaries from a single RGB image despite clutter, occlusion, viewpoint variation, and challenging geometric configurations.
Source: §1 · p. 1, §3.1 · pp. 4–5Method
CFILE uses an MFCN trained jointly on layout and semantic surface labels, then refines its coarse estimate by detecting critical lines, generating hypotheses, and ranking them with spatial constraints.
Source: §3.1 · p. 5, §3.2 · p. 6Results
CFILE achieves state-of-the-art performance, outperforming the second-best algorithm by 1.16% on Hedau’s dataset and 1.32% on the LSUN dataset.
Source: §4.2 · p. 11Takeaways & Limitations
Joint contour and surface learning plus constrained refinement provides robust layout results, including clearer occluded-region estimates that are less affected by object boundaries.
Source: §4.2 · pp. 10–11, §5 · p. 14A wrong coarse layout can mislead fine refinement, and the method reports a worst example with 79.4% accuracy; it also relies on the Manhattan World assumption.
Source: §3.1 · p. 4, §4.2 · p. 14
Abstract
from arXiv · showhide
The task of estimating the spatial layout of cluttered indoor scenes from a single RGB image is addressed in this work. Existing solutions to this problems largely rely on hand-craft features and vanishing lines, and they often fail in highly cluttered indoor rooms. The proposed coarse-to-fine indoor layout estimation (CFILE) method consists of two stages: 1) coarse layout estimation; and 2) fine layout localization. In the first stage, we adopt a fully convolutional neural network (FCN) to obtain a coarse-scale room layout estimate that is close to the ground truth globally. The proposed FCN considers combines the layout contour property and the surface property so as to provide a robust estimate in the presence of cluttered objects. In the second stage, we formulate an optimization framework that enforces several constraints such as layout contour straightness, surface smoothness and geometric constraints for layout detail refinement. Our proposed system offers the state-of-the-art performance on two commonly used benchmark datasets.
1 Introduction
Indoor layout estimation locates floor, wall, and ceiling boundaries from a single RGB image, a difficult task when clutter occludes boundaries. CFILE addresses this with coarse neural estimation followed by constraint-based refinement and reports state-of-the-art benchmark performance.
1 Introduction
- CFILE estimates indoor room layout from a single RGB image by locating floor, wall, and ceiling boundaries despite clutter and occlusion.The task supports applications including indoor navigation, object detection, and augmented reality.Source: §1 · p. 1
- The method combines coarse layout estimation with fine layout localization in a two-stage pipeline.An MFCN produces a coarse estimate, followed by optimization that refines layout details using contour, surface, and geometric constraints.Source: §1 · pp. 1–2
- The MFCN jointly uses layout contours and semantic surface properties to produce robust coarse estimates in cluttered scenes.This design is intended to remain close to the global ground truth while objects obscure boundaries.Source: §1 · p. 2
- The refinement framework enforces surface smoothness, contour straightness, and proper geometric structure.These constraints address the spatial reasoning limitations of convolutional estimation.Source: §1 · p. 2
- CFILE outperforms the second-best method by 1.16% on Hedau’s dataset and 1.32% on the LSUN dataset.The evaluation compares CFILE with several benchmarking methods and reports state-of-the-art performance.Source: §1 · p. 3
2 Related Work
Indoor layout estimation research has progressed from structured hypothesis ranking and hand-crafted cues toward CNN/FCN-based representations. Existing approaches combine learned or engineered evidence with inference and geometric constraints, but clutter and spatial reasoning remain central challenges.
2 Related Work
- Structured-learning methods generate layout hypotheses and rank them using features such as line membership, geometric context, object location, and vanishing lines.Hedau et al. introduced a representative two-stage formulation, while later work developed additional rejection criteria and scoring features.Source: §2 · p. 3, layout ranking · p. 3
layout ranking.
- Layout-ranking research incorporates objects, Manhattan junctions, camera models, room boxes, frames, and furniture to evaluate or reject hypotheses.These methods include conditional random fields, object-aware constraints, and branch-and-bound score optimization.Source: layout ranking · pp. 3–4
- Other approaches use 3D spatial rules, scene graphs, RGB-D exemplars, deformable cuboids, or human action and location from video.These methods infer room geometry jointly with object, scene, or temporal information.Source: layout ranking · p. 4
- CNN- and FCN-based methods learn informative edges or semantic surface labels, then refine results with vanishing lines or inference models.FCNs improve learned representations, but CNNs remain weak at imposing spatial constraints and conducting spatial inference.Source: layout ranking · p. 4
3 Coarse-to-Fine Indoor Layout Estimation (CFILE)
CFILE estimates cluttered indoor layouts coarse-to-fine: an MFCN predicts a robust global layout, then hypothesis generation and ranking refine boundaries using image evidence and geometric reasoning.
3.1 System Overview
- The Manhattan layout model uses four boundary lines and a vanishing point, but boundaries may lie outside the image or be occluded by objects.These cases make floor and wall boundary recovery particularly difficult.Source: §3.1 · pp. 4–5
3.2 Coarse Layout Estimation via MFCN
- CFILE first uses an MFCN to jointly learn layout contours and semantic surfaces, producing a coarse layout robust to clutter.Its two branches learn layout and floor, wall, and ceiling surface labels together; excluding object labels helps align contours across occluded regions.Source: §3.1 · p. 5, §3.2 · pp. 5–6, Figure 3 · p. 6
3.3 Layout Refinement
- The coarse MFCN layout is noisy because convolution and pooling thicken contours, reduce spatial resolution, and make occluded floor boundaries difficult to detect.Refinement therefore generates hypotheses near the global optimum before selecting a final layout.Source: §3.3 · p. 7
Generation of High-Quality Layout Hypotheses
- Critical-line hypotheses combine detected vanishing lines with geometrically or statistically recovered undetected and occluded lines, then rank layouts using the coarse probability map.The highest-scoring layout is selected as the final result; the score favors alignment with the coarse layout.Source: Generation of High-Quality Layout… · pp. 7–9
4 Experiments
Experiments evaluate CFILE on Hedau and LSUN using coarse-layout metrics, visual comparisons, and full-method benchmarking. CFILE achieves state-of-the-art performance, while visual analyses show robust coarse estimates and refinement of occluded boundaries, alongside failure cases caused by incorrect coarse estimates or confusing boundaries.
4.1 Experimental Setup
- Evaluation uses pixel-wise and corner errors on Hedau and LSUN data, with LSUN assessment restricted to its 394-image validation set because test ground truth is unavailable.Images are resized to 404 × 404 for MFCN training, and separate coarse models are trained for the two datasets.Source: §4.1 · pp. 9–10
4.2 Experimental Results and Discussion
- The coarse MFCN1 and MFCN2 schemes obtain higher ODS and OIS scores than the FCN baseline, providing more accurate regions for vanishing-line hypothesis generation.Table 1 compares the proposed settings with the informative edge method denoted FCN.Source: §4.2 · p. 10, Table 1 · p. 10
- MFCN2 produces more robust and clearer coarse layout contours in occluded regions than the FCN baseline across Hedau and LSUN examples.The comparison uses input images, FCN outputs, MFCN2 outputs, and ground truth.Source: §4.2 · pp. 10–11, Figure 6 · p. 11
- CFILE achieves state-of-the-art performance, outperforming the second-best algorithm by 1.16% on Hedau’s dataset and 1.32% on the LSUN dataset.The full algorithm includes coarse layout estimation, layout optimization, and ranking.Source: §4.2 · p. 11, Table 2 · p. 14, Table 3 · p. 14
- The refinement algorithm recovers occluded boundaries, selects among possible layouts, and remains robust in several highly cluttered Hedau rooms.These behaviors are illustrated among the six best CFILE results.Source: §4.2 · p. 11, Figure 7 · p. 12
- CFILE can be misled by an incorrect coarse estimate, identical-looking walls, confusing wall boundaries, or ambiguous ceiling lines; one worst case reaches 79.4% accuracy.The reported 79.4% example remains higher than the 61.05% worst example reported in.Source: §4.2 · p. 14
5 Conclusion and Future Work
The conclusion presents CFILE as a coarse-to-fine RGB indoor-layout method combining multi-task convolutional estimation with constraint-based refinement. Experiments support best performance on two benchmark datasets, while future work concerns multi-scale effects in CNN-based vision solutions.
5 Conclusion and Future Work
- CFILE combines an MFCN coarse estimate with optimization enforcing contour straightness, surface smoothness, and geometric constraints for layout refinement.The MFCN jointly trains on layout and semantic surface information.Source: §5 · p. 14
- Experimental results show that CFILE offers the best performance on two commonly used benchmark datasets.Source: §5 · p. 14
- The paper identifies the multi-scale effect of CNN-based vision solutions, semantic segmentation, and indoor-scene geometric layout as an interesting future topic.Source: §5 · pp. 14–15