Semi-Supervised Deep Learning for Monocular Depth Map Prediction (1702.02706v3)

Abstract: Supervised deep learning often suffers from the lack of sufficient training data. Specifically in the context of monocular depth map prediction, it is barely possible to determine dense ground truth depth images in realistic dynamic outdoor environments. When using LiDAR sensors, for instance, noise is present in the distance measurements, the calibration between sensors cannot be perfect, and the measurements are typically much sparser than the camera images. In this paper, we propose a novel approach to depth map prediction from monocular images that learns in a semi-supervised way. While we use sparse ground-truth depth for supervised learning, we also enforce our deep network to produce photoconsistent dense depth maps in a stereo setup using a direct image alignment loss. In experiments we demonstrate superior performance in depth map prediction from single images compared to the state-of-the-art methods.

• The paper introduces a semi-supervised approach that integrates sparse ground truth with unsupervised image alignment for improved depth estimation.
• It utilizes a deep residual encoder-decoder with long skip connections and a pre-trained encoder to capture finer details in depth maps.
• Experimental results on KITTI demonstrate lower RMSE and superior accuracy, indicating significant advances in monocular depth prediction.

Semi-Supervised Deep Learning for Monocular Depth Map Prediction

The paper "Semi-Supervised Deep Learning for Monocular Depth Map Prediction" introduces a novel approach to estimating depth from single monocular images using a semi-supervised methodology. This paper is situated within the broader context of depth estimation, a critical component in various computer vision applications including autonomous driving, 3D reconstruction, and augmented reality.

Core Contributions and Methodology

The authors address a fundamental issue in supervised deep learning for depth map prediction: the scarcity and imperfection of training data. Typically, gathering dense ground truth data, especially in dynamic outdoor environments, is challenging. LiDAR sensors, although commonly used, produce sparse and noisy measurements that complicate the training of accurate models. To mitigate these challenges, the authors propose a semi-supervised network that leverages both sparse ground-truth depth data and unsupervised image alignment in a stereo setup.

1. Network Architecture: The proposed system builds on a deep residual encoder-decoder framework enhanced by long skip connections, facilitating finer detail in depth map prediction. The encoder is pre-trained on ImageNet to capture intricacies effectively, while the decoder uprights these features for accurate depth prediction.
2. Loss Function: A unique aspect of the approach is the loss function, which integrates supervised and unsupervised components. The supervised component penalizes deviation from sparse ground truth, while the unsupervised component enforces photometric consistency between stereo image pairs using direct image alignment. Additionally, a regularization term reduces noise in textureless areas.
3. Training Strategy: The network is trained using a combination of pretrained weights for the encoder and a gradual integration of unsupervised cues, allowing it to converge efficiently from limited supervised data.

Experimental Evaluation

The proposed method demonstrates superior performance on the KITTI benchmark, outperforming state-of-the-art methods quantitatively and qualitatively. The network achieves lower Root Mean Square Error (RMSE) and superior accuracy metrics compared to previous supervised and unsupervised methodologies. Significant improvements are attributed to the integration of unsupervised learning, which augments the sparse data coverage and contributes positively to the depth prediction quality in monocular setups.

Implications and Future Work

This semi-supervised approach presents several implications for the field:

• Practical Utility: By reducing the dependency on dense ground truth data, this method enhances the applicability of depth estimation models in real-world scenarios where acquiring extensive datasets is infeasible.
• Theoretical Insights: The seamless integration of supervised and unsupervised signals in the loss function may inspire analogous strategies in other domains such as semantic segmentation or object detection.
• Generalization: While the model is trained predominantly on urban outdoor scenes (KITTI), its architecture and training pipeline can potentially be adapted for other environments and tasks—highlighting avenues for transfer learning or domain adaptation studies.

Future work could explore fine-tuning the model in diverse datasets or environments to test its generalization further, integrating additional cues like motion to handle dynamic scenes more effectively, or extending the semi-supervised framework to other computer vision tasks beyond depth estimation.

Overall, this research provides a robust framework that combines the benefits of supervised and unsupervised learning, pushing the boundaries of what's achievable in monocular depth map prediction.