Learning Less is More - 6D Camera Localization via 3D Surface Regression (1711.10228v2)

Abstract: Popular research areas like autonomous driving and augmented reality have renewed the interest in image-based camera localization. In this work, we address the task of predicting the 6D camera pose from a single RGB image in a given 3D environment. With the advent of neural networks, previous works have either learned the entire camera localization process, or multiple components of a camera localization pipeline. Our key contribution is to demonstrate and explain that learning a single component of this pipeline is sufficient. This component is a fully convolutional neural network for densely regressing so-called scene coordinates, defining the correspondence between the input image and the 3D scene space. The neural network is prepended to a new end-to-end trainable pipeline. Our system is efficient, highly accurate, robust in training, and exhibits outstanding generalization capabilities. It exceeds state-of-the-art consistently on indoor and outdoor datasets. Interestingly, our approach surpasses existing techniques even without utilizing a 3D model of the scene during training, since the network is able to discover 3D scene geometry automatically, solely from single-view constraints.

• The paper presents a fully convolutional neural network that efficiently predicts dense scene coordinates from a single RGB image, streamlining the camera localization pipeline.
• It employs a novel soft inlier count for hypothesis scoring, enhancing generalization and reducing overfitting compared to traditional methods.
• The approach achieves state-of-the-art accuracy on datasets like 7Scenes and Cambridge Landmarks without relying on a complete 3D model, making it ideal for practical applications.

Analysis of "Learning Less is More -- 6D Camera Localization via 3D Surface Regression"

This paper by Brachmann and Rother addresses the challenging problem of 6D camera localization using a single RGB image within a known 3D environment. It re-evaluates the complexity involved in camera pose estimation, suggesting that focusing on a single component of the localization pipeline can suffice for highly accurate results. The key proposition is a fully convolutional neural network (CNN) for dense regression of scene coordinates, which effectively captures the correspondence between the RGB image and the 3D scene space.

Key Contributions

The main innovation presented in this paper is the simplification of the camera localization pipeline. Instead of adopting an approach where the entire pipeline or multiple components are learning-driven, the authors suggest that learning the scene coordinate regression alone is sufficient. This is significant as it streamlines the computational process without sacrificing accuracy.

1. Fully Convolutional Neural Network for Scene Coordinate Regression: By employing a fully convolutional architecture, the authors manage to predict a dense map of scene coordinates efficiently. This avoids the inefficiencies of earlier methods that predicted coordinates one patch at a time, allowing for better resource utilization and faster inference times.
2. New Hypothesis Scoring Method: The use of a soft inlier count instead of a separate scoring CNN is an intelligent choice to mitigate overfitting and improve generalization. This method evaluates consensus among hypothesized pose estimates based on the inlier threshold, thus avoiding potential pitfalls associated with overfitting specific spatial patterns of reprojection errors.
3. Training Without a 3D Scene Model: A notable advancement is the system's ability to learn scene coordinate regression without any 3D model or RGB-D data, relying solely on RGB images with known poses. This is particularly beneficial as it circumvents the often arduous task of generating or acquiring a precise 3D model, especially for large or complex environments.
4. Improvements in Inference Stability and Accuracy: With enhanced end-to-end training stability due to an analytical approximation of pose refinement gradients, this method achieves not only superior accuracy compared to state-of-the-art methods but does so with a more stable learning process.

Experimental Validation

Through extensive testing on datasets like 7Scenes, 12Scenes, and Cambridge Landmarks, the paper validates the efficiency and accuracy of their proposed system. The results consistently demonstrate superior performance over existing methods, including those relying on sparse feature-based approaches or trained on RGB-D data. The ability to achieve this level of accuracy without a 3D model is particularly emphasized.

Implications and Future Directions

Practically, this method exemplifies a significant reduction in the complexity and resource demands for camera localization systems. The findings imply potential reductions in data requirements, paving the way for broader adoption in applications such as augmented reality or autonomous navigation, where generating and storing comprehensive 3D models can be prohibitive.

Theoretically, the approach opens up questions about the potential of simplifying other complex computer vision and machine learning pipelines. Future research might investigate the applicability of this method in environments with even more challenging dynamics or explore how such a simplification can be leveraged in other domains.

Concluding Remarks

Brachmann and Rother's work on 6D camera localization via 3D surface regression offers a compelling simplification of the localization pipeline that maintains state-of-the-art performance. This approach provides both practical advantages and theoretical insights, suggesting new directions for research in the field of computer vision. It stands as a testament to the potential of refining specific components of complex systems, emphasizing efficiency without compromising on accuracy.