Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera (1907.05820v2)

Abstract: We present GLNet, a self-supervised framework for learning depth, optical flow, camera pose and intrinsic parameters from monocular video - addressing the difficulty of acquiring realistic ground-truth for such tasks. We propose three contributions: 1) we design new loss functions that capture multiple geometric constraints (eg. epipolar geometry) as well as an adaptive photometric loss that supports multiple moving objects, rigid and non-rigid, 2) we extend the model such that it predicts camera intrinsics, making it applicable to uncalibrated video, and 3) we propose several online refinement strategies that rely on the symmetry of our self-supervised loss in training and testing, in particular optimizing model parameters and/or the output of different tasks, thus leveraging their mutual interactions. The idea of jointly optimizing the system output, under all geometric and photometric constraints can be viewed as a dense generalization of classical bundle adjustment. We demonstrate the effectiveness of our method on KITTI and Cityscapes, where we outperform previous self-supervised approaches on multiple tasks. We also show good generalization for transfer learning in YouTube videos.

• The paper introduces GLNet, a novel self-supervised framework using geometric constraints, adaptive photometric loss, and online refinement to jointly learn depth, optical flow, camera pose, and intrinsics from monocular video without ground truth.
• Experiments on KITTI and Cityscapes datasets show GLNet outperforms existing self-supervised methods, demonstrating enhanced scene reconstruction and superior cross-domain transfer, especially for uncalibrated videos.
• GLNet's ability to handle uncalibrated video and predict intrinsics makes it highly relevant for practical applications like autonomous driving and robotics by bridging deep learning and classical geometric principles.

An Analysis of GLNet: Self-supervised Learning for Depth, Optical Flow, and Camera Parameters from Monocular Video

In the paper titled "Self-supervised Learning with Geometric Constraints in Monocular Video Connecting Flow, Depth, and Camera," the authors propose a novel self-supervised framework, GLNet, aimed at addressing the challenges of learning depth, optical flow, camera pose, and intrinsic parameters from monocular video. The work primarily focuses on overcoming the limitations associated with acquiring realistic ground truth data for these tasks. This summary presents an analytical overview of the contributions, methodologies, and implications of their research.

Contributions and Methodology

The paper introduces three key contributions to the domain of self-supervised learning in 3D scene understanding. Firstly, the authors have engineered new loss functions that incorporate geometric constraints such as epipolar geometry, along with an adaptive photometric loss function designed to accommodate both rigid and non-rigid moving objects. Secondly, the model is extended to predict camera intrinsics, which renders it applicable to uncalibrated video. Thirdly, an innovative strategy for online refinement is proposed. This strategy utilizes the symmetry of the self-supervised loss during both training and testing, optimizing both model parameters and task outputs by exploiting their interdependencies.

GLNet represents an integration of self-supervised deep learning systems, which do not require labeled data, and classical structure-from-motion techniques, which are grounded in explicit geometric relations. The framework formulates an optimization objective that combines photometric and geometric loss components, reflecting a dense generalization of bundle adjustment. Each output of the system, including depth, camera pose, and intrinsics, can be jointly refined under the established constraints.

Experimental Validation

The authors validate the effectiveness of GLNet through comprehensive experiments conducted on standard datasets such as KITTI and Cityscapes. GLNet exhibits superior performance in comparison to existing self-supervised approaches across multiple tasks, demonstrating enhanced capability in both scene reconstruction and cross-domain transfer learning. Particularly, GLNet outperforms in scenarios involving uncalibrated videos, showcasing its robustness to varying camera parameters.

A detailed ablation paper evaluates the influence of various components in the model. The results highlight that the geometric losses contribute significantly to the improvements observed in both depth estimation and optical flow, underscoring the importance of integrating classical geometric constraints in deep learning frameworks.

Implications and Future Directions

The framework’s ability to predict camera intrinsics and its application to uncalibrated video extends its appeal to practical fields such as autonomous driving and robotics, where labeled data is often scarce or impractical to obtain. Furthermore, the generalization capability of GLNet indicates potential for application in diverse environments, making it a versatile model for 3D scene understanding. The methodology laid in this work paves the way for more sophisticated self-supervised frameworks that could incorporate even deeper geometric insights and constraints.

Future research could explore further optimization of self-supervised objectives to reduce computational expense, particularly in real-time applications. Additionally, there is potential to investigate the integration of other modalities or additional cues that might enhance the model's feature extraction and prediction accuracy without the need for explicit supervision.

Overall, GLNet is a significant step in advancing self-supervised learning methodologies by bridging the gap between intrinsic deep learning capabilities and established geometric principles, thereby fostering a more cohesive approach to understanding and reconstructing dynamic 3D environments.