Recurrent Neural Network for (Un-)supervised Learning of Monocular VideoVisual Odometry and Depth (1904.07087v1)

Abstract: Deep learning-based, single-view depth estimation methods have recently shown highly promising results. However, such methods ignore one of the most important features for determining depth in the human vision system, which is motion. We propose a learning-based, multi-view dense depth map and odometry estimation method that uses Recurrent Neural Networks (RNN) and trains utilizing multi-view image reprojection and forward-backward flow-consistency losses. Our model can be trained in a supervised or even unsupervised mode. It is designed for depth and visual odometry estimation from video where the input frames are temporally correlated. However, it also generalizes to single-view depth estimation. Our method produces superior results to the state-of-the-art approaches for single-view and multi-view learning-based depth estimation on the KITTI driving dataset.

• The paper introduces an RNN framework incorporating ConvLSTM units for both supervised and unsupervised learning of monocular video visual odometry and depth.
• Experimental results on the KITTI dataset demonstrate superior depth estimation performance compared to leading single and two-view methods, especially in multi-view evaluations.
• The framework's dual capacity for supervised/unsupervised learning and ability to handle arbitrary sequence lengths make it adaptable for real-time applications and scenarios with limited ground truth data.

Overview of Recurrent Neural Network for (Un-)supervised Learning of Monocular Video Visual Odometry and Depth

In the domain of computer vision, the estimation of depth and visual odometry from video sequences is fundamental for applications such as autonomous driving and augmented reality. The paper "Recurrent Neural Network for (Un-)supervised Learning of Monocular Video Visual Odometry and Depth" presents an innovative approach to this task by utilizing Recurrent Neural Networks (RNNs) for both supervised and unsupervised settings. This methodology enhances depth and visual odometry estimations by leveraging temporal correlations in monocular video sequences, contrary to the traditional single-view depth estimation approaches that often ignore motion as a critical human perceptual feature.

Key Contributions

The RNN architecture integrates convolutional Long Short-Term Memory (ConvLSTM) units, facilitating the retention of temporal information across frames—something pivotal for sequences taken by monocular cameras. This framework not only supports depth map generation but also enables visual odometry estimation through consecutive video frames alignment, which is used to maximize the effectiveness of multi-view image reprojection constraints and forward-backward flow-consistency losses. The ability to seamlessly adjust between supervised and unsupervised training modes empowers the framework to be adaptable and robust across varying data availability scenarios.

Noteworthy innovations include:

• RNN Architecture: The ConvLSTM units are strategically interwoven with convolutional layers to optimize multi-view constraints and temporal sequence evaluation.
• Loss Functions: Multi-view image reprojection and forward-backward flow-consistency are meticulously tailored to capitalize on the temporal scope provided by video sequences.
• Scalability: The framework is designed to operate beyond fixed-length sequences, providing a consistent scene scale across arbitrary lengths, which has been quantitatively validated.

Experimental Validation

The researchers validated their framework using the KITTI dataset, a prominent benchmark for assessing autonomous driving systems. Quantitative analysis shows superior performance in depth estimation when compared against the leading single-view and two-view methods—utilizing both supervised and unsupervised training setups. Particularly, the multi-view evaluation demonstrates significant improvements over traditional methodologies, showcasing robustness in handling sequences where ground truth data is sparse or unavailable.

Implications and Future Directions

This research has practical implications for real-time applications, such as dynamic environment navigation, where continuous and precise depth estimations are crucial. Moreover, the framework's dual capacity for both supervised and unsupervised learning paradigms introduces possibilities for expansive deployment in scenarios where labeled data is limited or expensive to acquire.

Looking forward, additional exploration into optimizing the computational efficiency of the framework might be valuable, especially considering the dense training data requirement. Furthermore, extending this methodology to stereo cameras and integrating semantic understanding could enrich its application breadth, potentially leading to advancements in AI-driven spatial awareness systems.

This paper firmly contributes to advancing AI capabilities in interpreting complex visual environments by establishing a rigorous yet flexible approach to video-based depth and visual odometry estimation.