Source-linked AI summary
RoomNet: End-to-End Room Layout Estimation
Chen-Yu Lee, Vijay Badrinarayanan, Tomasz Malisiewicz, Andrew Rabinovich
TL;DR
Monocular room layout estimation traditionally uses semantic segmentation and iterative hypothesis ranking. This paper instead predicts ordered layout keypoints and room type with RoomNet, an end-to-end encoder-decoder, achieving state-of-the-art results on Hedau and LSUN with 200× to 600× speedup.
Problem
Room layout estimation requires recovering a structured 2D indoor-scene representation from a monocular image, while prior approaches use multi-step segmentation and hypothesis-ranking pipelines.
Method
RoomNet jointly predicts room layout keypoint heatmaps and room type, then connects ordered keypoints to obtain the layout and its surface segmentation.
Results
RoomNet achieves state-of-the-art performance on Hedau and LSUN with 200× to 600× speedup compared with the most recent work.
Takeaways & Limitations
The keypoint formulation enables end-to-end, accurate, and efficient room layout estimation without the geometry-inspired multi-step processing used by much prior work.
Takeaways & Limitations
RoomNet’s major failure cases occur when layout boundaries are barely visible or when multiple room-layout explanations are plausible; skipping layers also does not improve performance on the relatively small training set.
Abstract
from arXiv · showhide
This paper focuses on the task of room layout estimation from a monocular RGB image. Prior works break the problem into two sub-tasks: semantic segmentation of floor, walls, ceiling to produce layout hypotheses, followed by an iterative optimization step to rank these hypotheses. In contrast, we adopt a more direct formulation of this problem as one of estimating an ordered set of room layout keypoints. The room layout and the corresponding segmentation is completely specified given the locations of these ordered keypoints. We predict the locations of the room layout keypoints using RoomNet, an end-to-end trainable encoder-decoder network. On the challenging benchmark datasets Hedau and LSUN, we achieve state-of-the-art performance along with 200x to 600x speedup compared to the most recent work. Additionally, we present optional extensions to the RoomNet architecture such as including recurrent computations and memory units to refine the keypoint locations under the same parametric capacity.
1. Introduction
RoomNet reframes monocular room layout estimation from a multi-step, hypothesis-ranking pipeline as direct ordered keypoint prediction. A CNN infers room type and keypoints, which are connected to recover the layout and its segmentation, achieving state-of-the-art benchmark performance with substantial speedup.
- Room layout estimation supports indoor navigation, scene reconstruction and rendering, and augmented reality from monocular images.
- Prior methods rely on bottom-up image cues, vanishing-point detection, and post-processing to generate and rank many layout hypotheses.These low-level features are susceptible to local noise, scene clutter, and occlusion.
- The room-type representation indexes 11 layouts, with keypoint ordering defining the connectivities needed to construct each layout.
- RoomNet directly infers room layout keypoints and room type, then connects ordered keypoints to produce the 2D layout.Its convolutional encoder-decoder processes a 320 × 320 image, while layout-surface segmentation follows from the keypoint connectivity.
- RoomNet reports state-of-the-art performance on Hedau and LSUN with 200× to 600× speedup over the most recent work.
2. RoomNet
RoomNet reformulates room layout estimation as direct prediction of ordered 2D keypoints and an associated room type, using an efficient encoder-decoder architecture. A recurrent memory-augmented extension iteratively refines keypoint heatmaps while sharing weights across time steps.
- Keypoint representation: RoomNet represents a room layout by ordered keypoint locations and connects them in a prescribed order after predicting the associated room type.The representation covers 11 room layouts under typical camera poses and Manhattan-world cuboid assumptions.
- Base architecture: RoomNet adapts a SegNet-style encoder-decoder, using bottleneck features and a trimmed decoder to model layout effectively at 40 × 40 while reducing computation.The decoder upsamples bottleneck features from 10 × 10 to 40 × 40 rather than the full input resolution.
- Joint prediction: The model jointly trains keypoint heatmap regression with Euclidean loss and room-type classification with cross-entropy loss.A side head predicts the room type, whose predicted index selects the corresponding keypoint heatmaps for regression.
- Recurrent refinement: The memory-augmented recurrent encoder-decoder refines keypoint heatmaps over artificial time steps using hidden units and shared convolutional weights.Its recurrent structure exploits contextual and structural relationships among keypoints while maintaining a fixed number of parameters.
- Recurrent refinement: Refinement produces cleaner, more concentrated heatmaps and removes some false positives, including correcting errors made in earlier predictions.The paper analyzes this refinement against the non-recurrent base model.
3. Experiments
RoomNet is evaluated on Hedau and LSUN using standardized training, runtime, accuracy, ablation, and qualitative analyses. The experiments report state-of-the-art accuracy, large speedups, benefits from recurrent refinement and deep supervision, and failure cases tied to ambiguous or barely visible boundaries.
- Datasets and setup: RoomNet is evaluated on Hedau and LSUN using the same experimental setup as Dasgupta et al.Hedau contains 209 training, 53 validation, and 105 test images; LSUN contains 4000 training, 394 validation, and 1000 test images.
- Datasets and setup: RoomNet outputs 40 × 40 keypoint heatmaps and an associated room-type label from 320 × 320 RGB inputs.Keypoint locations are selected from the maximum response in each heatmap.
- Accuracy: RoomNet achieves state-of-the-art performance on both Hedau and LSUN, improving over the previous best method on keypoint and pixel errors.The comparison is against the two-step framework of Dasgupta et al.
- Runtime and complexity: 200× speedup is achieved by the full recurrent model, while the basic RoomNet achieves 600× speedup over the previous best method.The runtime evaluation uses 320×320 inputs and timing for two forward passes.
- Recurrent refinement: 6.3% keypoint error and 9.86 pixel error are achieved by the recurrent model, compared with 6.95% and 10.46% without recurrent refinement.No further significant improvement is observed after three iterations, and convolutional weights are shared across iterations.
- Deep supervision through time: Deep supervision through time immediately reduces both keypoint and pixel errors, especially as recurrent iterations increase.Without deep supervision, the 3-iteration model performs worse than the 2-iteration model; applying it rectifies this behavior.
- Qualitative results: RoomNet is robust to object occlusion but fails mainly when layout boundaries are barely visible or multiple room-layout explanations are plausible.Qualitative results illustrate these cases on LSUN.
4. Discussion
The discussion compares encoder-decoder variants for RoomNet, finding benefits from stacking, feedback, and recurrent memory while skip connections and additional feedback provide no improvement in specific configurations.
- Alternative encoder-decoders: Stacking encoder-decoder networks improves performance by encouraging the network to learn spatial keypoint structure across multiple bottleneck layers.
- Alternative encoder-decoders: Skip connections do not improve performance for this task, possibly because the training set contains only thousands of examples rather than millions.
- Alternative encoder-decoders: Feedback improves performance by supplying each iteration with the original image and the previous prediction.
- Alternative encoder-decoders: The memory-augmented recurrent encoder-decoder achieves the lowest keypoint and pixel errors among the evaluated variants.
- Alternative encoder-decoders: Adding feedback to the memory-augmented recurrent encoder-decoder does not improve results, possibly because its memory already stores prior hidden-state information.
5. Conclusion
The paper formulates room layout estimation directly as keypoint localization and trains RoomNet and its extensions end-to-end for accurate and efficient estimation.
- RoomNet presents room layout estimation as a simple, direct keypoint localization problem.
- RoomNet and its extensions are trained end-to-end to perform accurate and efficient room layout estimation.