Corners for Layout: End-to-End Layout Recovery from 360 Images (1903.08094v2)

Abstract: The problem of 3D layout recovery in indoor scenes has been a core research topic for over a decade. However, there are still several major challenges that remain unsolved. Among the most relevant ones, a major part of the state-of-the-art methods make implicit or explicit assumptions on the scenes -- e.g. box-shaped or Manhattan layouts. Also, current methods are computationally expensive and not suitable for real-time applications like robot navigation and AR/VR. In this work we present CFL (Corners for Layout), the first end-to-end model for 3D layout recovery on 360 images. Our experimental results show that we outperform the state of the art relaxing assumptions about the scene and at a lower cost. We also show that our model generalizes better to camera position variations than conventional approaches by using EquiConvs, a type of convolution applied directly on the sphere projection and hence invariant to the equirectangular distortions. CFL Webpage: https://cfernandezlab.github.io/CFL/

• The paper presents an end-to-end model that recovers 3D room layouts from 360 images over 100 times faster than traditional methods.
• It employs novel EquiConvs to handle equirectangular distortions, ensuring robust performance across diverse camera positions.
• The approach reduces strict geometric assumptions and achieves superior layout accuracy on benchmarks like SUN360 and Stanford 2D-3D.

Insights into "Corners for Layout: End-to-End Layout Recovery from 360 Images"

The paper "Corners for Layout: End-to-End Layout Recovery from 360 Images" presents an innovative approach in the domain of computer vision by tackling the challenging task of recovering the 3D layout of indoor scenes from single 360-degree images. Traditional methods have largely been constrained by assumptions of simplified geometries, such as the Manhattan or box-shaped layouts, which do not accurately reflect the complexity found in real-world environments. Furthermore, these methods are often computationally intensive, thereby hindering their applicability to real-time applications in areas such as robot navigation and augmented/virtual reality (AR/VR).

The authors introduce Corners for Layout (CFL), an end-to-end deep learning model that recovers room layouts from 360-degree images with significant improvements in both efficiency and accuracy. By employing EquiConvs, a novel convolution operation that accounts for the distortions inherent in spherical imagery, the model demonstrates better generalization capabilities across varied camera positions and orientations compared to conventional approaches.

Core Innovations

1. End-to-End Learning Architecture:
   • CFL is designed to output a map of room corners from which the 3D layout can be directly obtained without additional processing steps. This end-to-end structure allows CFL to process images over 100 times faster than existing methods.
2. Generalization via EquiConvs:
   • EquiConvs adapt the shape of convolutional kernels to better handle the distortions present in equirectangular projections, enhancing the model's robustness to camera rotation and positioning. This positions CFL uniquely to handle diverse scenes that deviate from traditional layout assumptions.
3. Reduced Assumptions:
   • Unlike traditional models reliant on Manhattan world assumptions, CFL is capable of predicting more complex room geometries, thereby aligning more closely with real-world applications where such assumptions frequently lead to inaccuracies.
4. Performance Metrics:
   • Experiments conducted on datasets such as SUN360 and Stanford 2D-3D showed CFL outperforming state-of-the-art approaches in layout accuracy metrics like intersection over union (3DIoU) and corner estimation errors.
5. Computational Efficiency:
   • The streamlined architecture significantly reduces computation time, vital for real-time implementation in dynamic environments.

Implications and Future Directions

The results of the paper underscore the potential for using end-to-end neural networks with geometric-awareness for layout recovery from 360-degree images. This has significant implications for disciplines that require detailed spatial understanding of environments. By eliminating the reliance on post-processing and strong scene simplifications, this method could be adapted for autonomous systems in robotics, enhancing their capability to perceive and navigate through complex surroundings without manual intervention.

Furthermore, the implementation of EquiConvs could influence the design of convolutional operations in other domains where data is represented in spherical formats, such as climate modeling or omnidirectional imaging in robotics. Future research may consider integrating this framework with other learning paradigms, such as reinforcement learning, to further empower autonomous agents with predictive spatial awareness reshaped by end-to-end learning of environment geometries. Such advancements could also extend to applications within AR/VR where rapid and accurate environmental modeling enhances user experiences.

In conclusion, the CFL framework marks an important progression in the field of computer vision, offering a scalable and efficient solution to a longstanding problem while setting a promising direction for future research endeavors in the processing of 360-degree visual data.