Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation

Abstract: We address the unsupervised learning of several interconnected problems in low-level vision: single view depth prediction, camera motion estimation, optical flow, and segmentation of a video into the static scene and moving regions. Our key insight is that these four fundamental vision problems are coupled through geometric constraints. Consequently, learning to solve them together simplifies the problem because the solutions can reinforce each other. We go beyond previous work by exploiting geometry more explicitly and segmenting the scene into static and moving regions. To that end, we introduce Competitive Collaboration, a framework that facilitates the coordinated training of multiple specialized neural networks to solve complex problems. Competitive Collaboration works much like expectation-maximization, but with neural networks that act as both competitors to explain pixels that correspond to static or moving regions, and as collaborators through a moderator that assigns pixels to be either static or independently moving. Our novel method integrates all these problems in a common framework and simultaneously reasons about the segmentation of the scene into moving objects and the static background, the camera motion, depth of the static scene structure, and the optical flow of moving objects. Our model is trained without any supervision and achieves state-of-the-art performance among joint unsupervised methods on all sub-problems.

• The paper presents the Competitive Collaboration framework that jointly addresses depth, camera motion, optical flow, and motion segmentation by leveraging geometric interdependencies.
• It employs a dual-network strategy—with a static scene reconstructor and a moving region reconstructor moderated by a segmentation network—to optimize task-specific performance without supervision.
• Empirical results show state-of-the-art performance on benchmark datasets like KITTI and Cityscapes, highlighting significant improvements in unsupervised low-level vision tasks.

Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow, and Motion Segmentation

Overview

The paper presents an unsupervised learning framework for tackling several core challenges in low-level vision: depth prediction from a single image, camera motion estimation, optical flow computation, and motion segmentation. Unlike traditional approaches that independently address these problems, this research posits that solving them together can leverage the inherent geometric interdependencies to simplify the learning process. The paper introduces a novel methodology termed "Competitive Collaboration," which coordinates multiple specialized neural networks to work in tandem, ensuring each network contributes optimally to solving interconnected sub-tasks.

Methodology

The Competitive Collaboration framework resembles expectation-maximization but uses neural networks to manage both competition and cooperation. It consists of two players: a static scene reconstructor and a moving region reconstructor, which jointly solve the vision tasks with the help of a moderator network. The moderator segments the scene into static and moving regions, facilitating the division of labor between the competing networks while promoting collaboration to ensure fair data distribution.

The static scene reconstructor handles pixels that conform to the static scene model using depth and camera motion information, while the moving region reconstructor tackles independent object motion using optical flow. The introduction of a consensus mechanism ensures that moving object segmentation takes place without any explicit supervision, leveraging geometric information derived from depth, flow, and motion segmentation.

Empirical Results

The empirical evaluations confirm that the Competitive Collaboration framework achieves state-of-the-art performance across several benchmark tasks:

• Depth Prediction: The model outperforms existing methods in single-view depth estimation on the KITTI dataset, both when trained only on KITTI and when also utilizing Cityscapes data.
• Camera Motion Estimation: It shows competitive results in estimating camera motion on the KITTI Odometry dataset.
• Optical Flow: The approach delivers top-tier performance in unsupervised optical flow estimation, surpassing other joint methods and many specialized optical flow techniques.
• Motion Segmentation: The segmentation results demonstrate that the model accurately distinguishes between static and moving regions in images, validated on the KITTI 2015 dataset.

Implications

This research highlights the powerful synergies that can be harnessed by jointly solving geometric vision tasks. The Competitive Collaboration framework sets the stage for further advances in unsupervised learning where multiple tasks can inform and improve one another. The approach is particularly beneficial in scenarios where obtaining labeled data is impractical, as is common for continuous-valued outputs such as depth and flow.

Future Directions

Future work might integrate sparse supervision, further boosting model performance. Combining semantic information with motion segmentation could enhance the discernment of non-rigid motion. Additionally, extending the methodology to accommodate world coordinate systems could enable long-sequence integration, advancing applications like autonomous driving and augmented reality. The paper also suggests potential applications beyond automotive datasets, indicating broader applicability to arbitrary scenes and camera settings.

In conclusion, the paper presents a rigorous, innovative framework that advances the understanding and application of unsupervised learning in computer vision. The Competitive Collaboration strategy exemplifies how coordinated training can lead to sophisticated, integrated solutions for complex geometric tasks.