How do neural networks see depth in single images? (1905.07005v1)

Abstract: Deep neural networks have lead to a breakthrough in depth estimation from single images. Recent work often focuses on the accuracy of the depth map, where an evaluation on a publicly available test set such as the KITTI vision benchmark is often the main result of the article. While such an evaluation shows how well neural networks can estimate depth, it does not show how they do this. To the best of our knowledge, no work currently exists that analyzes what these networks have learned. In this work we take the MonoDepth network by Godard et al. and investigate what visual cues it exploits for depth estimation. We find that the network ignores the apparent size of known obstacles in favor of their vertical position in the image. Using the vertical position requires the camera pose to be known; however we find that MonoDepth only partially corrects for changes in camera pitch and roll and that these influence the estimated depth towards obstacles. We further show that MonoDepth's use of the vertical image position allows it to estimate the distance towards arbitrary obstacles, even those not appearing in the training set, but that it requires a strong edge at the ground contact point of the object to do so. In future work we will investigate whether these observations also apply to other neural networks for monocular depth estimation.

• The paper reveals that MonoDepth primarily uses an object's vertical position over apparent size to estimate depth.
• The paper shows that MonoDepth only partially corrects for camera pitch and roll, affecting performance in dynamic scenarios.
• The paper identifies that strong edges and shadows significantly enhance obstacle detection beyond traditional texture cues.

An Expert Analysis of "How do neural networks see depth in single images?"

The paper authored by Tom van Dijk and Guido de Croon presents a comprehensive investigation into the underlying mechanisms employed by neural networks, specifically focusing on the MonoDepth network by Godard et al., to estimate depth from single images. This inquiry elucidates how neural networks interpret depth cues and the extent to which these cues align with traditional vision-based approaches in both machines and humans.

The authors challenge the predominant approach in monocular depth estimation research that emphasizes accuracy evaluations against benchmark datasets like KITTI, instead prioritizing an understanding of the visual cues leveraged by these networks. Their paper reveals that MonoDepth primarily utilizes the vertical position of objects within an image, rather than apparent size, to gauge depth, an observation that contrasts with theoretical expectations and prior assumptions about pictorial depth cues.

Key Findings

1. Primary Depth Cue – Vertical Position: The paper demonstrates that MonoDepth exploits the vertical placement of objects as a primary depth cue, contrasting with the expected usage of apparent size as a depth indicator. This finding is substantiated through a series of tests involving control and altered image sets, revealing that positional changes affect depth estimation more significantly than variations in apparent size.
2. Partial Compensation for Camera Pose: The investigation further uncovers that MonoDepth only partially corrects for camera pose variations (pitch and roll), leading to notable shifts in estimated distance when such variations occur. This partial correction indicates a limitation in the network's adaptability to dynamic camera conditions, affecting the reliability of depth estimation in variable scenarios.
3. Role of Shadows and Edges in Object Detection: Another critical aspect explored is the network's capability to detect obstacles not explicitly present in the training set. It is found that the presence of strong edges and shadows significantly influences the network's ability to detect and correctly map the depth of such objects, regardless of traditional texture and color cues.

Implications and Future Directions

The insights from this paper have substantial implications for future approaches in monocular depth estimation. The reliance on vertical positioning as a primary cue may lead to biases in scenarios where camera calibration varies. Thus, networks like MonoDepth could benefit from enhanced adaptability to camera pose changes or more comprehensive training incorporating dynamic poses and varied environments.

Moreover, understanding the limitations regarding unfamiliar object detection signals a need for diversified training datasets that can robustly cover a wider array of object classes and environmental conditions. Such improvements can potentially enhance the generalization of these networks to real-world applications involving autonomous vehicles, robotics, and other fields requiring reliable depth perception from single images.

Moving forward, it will be crucial to apply similar analyses to other neural networks employed in depth perception, broadening our understanding of these models' interpretative methodologies and expanding their applicability across varying contexts. Through further research, more sophisticated and adaptive monocular depth estimation systems can be developed, rooted in a deeper comprehension of the visual strategies these networks deploy.