The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth (2104.14540v2)

Abstract: Self-supervised monocular depth estimation networks are trained to predict scene depth using nearby frames as a supervision signal during training. However, for many applications, sequence information in the form of video frames is also available at test time. The vast majority of monocular networks do not make use of this extra signal, thus ignoring valuable information that could be used to improve the predicted depth. Those that do, either use computationally expensive test-time refinement techniques or off-the-shelf recurrent networks, which only indirectly make use of the geometric information that is inherently available. We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available. Taking inspiration from multi-view stereo, we propose a deep end-to-end cost volume based approach that is trained using self-supervision only. We present a novel consistency loss that encourages the network to ignore the cost volume when it is deemed unreliable, e.g. in the case of moving objects, and an augmentation scheme to cope with static cameras. Our detailed experiments on both KITTI and Cityscapes show that we outperform all published self-supervised baselines, including those that use single or multiple frames at test time.

• The paper introduces a self-supervised model that leverages multi-frame data to significantly improve monocular depth estimation accuracy.
• It employs an adaptive cost volume and innovative consistency loss to address scale ambiguity and filter unreliable dynamic scene information.
• Experiments on KITTI and Cityscapes demonstrate ManyDepth's efficiency and robustness, offering promising applications in AR and autonomous driving.

Overview of "The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth" Paper

This paper addresses the challenge of depth estimation from monocular images using self-supervised learning, presenting ManyDepth, an approach that leverages multi-frame information during both training and inference. Existing monocular depth estimation methods largely neglect the additional data available from video sequences at test time, often relying solely on single-frame information. ManyDepth seeks to bridge this gap by incorporating sequence information to enhance depth prediction capabilities.

Contributions

1. Multi-Frame Depth Estimation Model: The paper introduces a novel self-supervised model that integrates multi-frame data to improve depth estimation performance. By utilizing a cost volume inspired by multi-view stereo techniques, ManyDepth processes sequential frames to provide improved depth maps.
2. Innovative Loss Functions: A key contribution is the design of a consistency loss that trains the network to ignore unreliable information from the cost volume, especially in scenarios involving motion, such as moving objects. This loss enables the system to prioritize more accurate depth information during inference.
3. Adaptive Cost Volume: The paper proposes an adaptive mechanism for determining the range of depths considered by the cost volume, addressing the scale ambiguity inherent in self-supervised learning from monocular sequences. This innovation allows the network to dynamically adjust its depth range based on the observed data, improving its flexibility and robustness.

Experimental Evaluation

Experiments demonstrate the efficacy of ManyDepth on benchmark datasets such as KITTI and Cityscapes. The method outperforms existing self-supervised monocular and multi-frame depth estimation baselines, achieving superior results without requiring expensive test-time refinements. Specifically, ManyDepth consistently shows lower absolute relative error and RMSE compared to baseline models.

Practical and Theoretical Implications

The practical implications of this research are significant, particularly for applications requiring real-time depth estimation, such as augmented reality and autonomous driving. The model’s ability to leverage sequential information means it can be deployed efficiently in dynamic environments encountered in these applications.

Theoretically, this work contributes to the growing body of literature on self-supervised learning by demonstrating how traditional multi-view stereo concepts can be adapted and integrated into monocular systems. It opens pathways for future studies to explore other geometric cues that can be incorporated into monocular depth networks.

Future Developments

The research invites further exploration into more sophisticated cost volume aggregation methods and the integration of additional modalities (e.g., motion predictions) to further enhance depth estimation accuracy. Additionally, as the field progresses, there is potential for these methods to be extended to other areas of computer vision, such as 3D reconstruction and scene understanding, reinforcing the relevance of the approach in advancing the capabilities of AI systems.