Cost Volume Depth Estimation With Teacher-Guided Cross-Attention
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Solution Overview
Problem
Existing depth estimation methods using cost volumes face accuracy issues with dynamic objects, low texture areas, and occluded objects, reducing the overall reliability of 3D representations for autonomous agents.
Innovation Solution
A self-supervised learning approach is employed to improve depth estimation by generating a cross-attention cost volume based on current and previous images, combining these features with single-frame features to enhance depth estimation accuracy, particularly for dynamic and occluded objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If cost volume based depth estimation is used, then depth estimation can be performed using multiple frames, but accuracy deteriorates for dynamic objects, low texture areas, and occluded objects
Solution Approach 1:
A teacher network is introduced as an intermediary component that generates pseudo-ground-truth depth maps from multiple frames. This teacher network acts as a mediator between the multi-frame input and the student network, providing supervised training signals that enable the student network to learn accurate depth estimation even for dynamic objects, low texture areas, and occluded objects where traditional cost volume methods fail
Solution Approach 2:
The patent changes the training paradigm from unsupervised to self-supervised by introducing pseudo-ground-truth depth maps generated by the teacher network. This parameter change in the training approach allows the system to leverage temporal information from multiple frames while maintaining accuracy for challenging cases through the supervised learning signal provided by the teacher network
2Speed
If single frame depth estimation is used, then processing speed is maintained, but accuracy is limited compared to multi-frame methods
Solution Approach 1:
The teacher network performs preliminary processing by generating pseudo-ground-truth depth maps from multiple frames before training the student network. This preliminary action creates a supervised learning signal that enables the student network to achieve both multi-frame accuracy and efficient inference, resolving the trade-off between processing speed and accuracy
Data Source
AI summary
A method for controlling an agent in an environment includes generating a cost volume based on a current image of the environment and one or more previous images of the environment. The method also includes generating a depth estimate based on integrating cost volume features of the cost volume with current image features of the current image. The method further includes controlling an action of the agent based on the depth estimate.