Cross-Attention Cost Volume Depth Estimation for Dynamic Scenes
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Solution Overview
Problem
Existing multi-frame depth estimation methods using cost volumes face challenges in accurately estimating depths for dynamic objects, low texture areas, and occluded objects, leading to reduced accuracy in 3D representation generation for autonomous agents.
Innovation Solution
The method employs a cross-attention model to generate a cross-attention cost volume based on current and previous images in a sequence, combining these features with single-frame features to improve depth estimation accuracy. This approach leverages self-supervised learning to enhance depth estimates, particularly for dynamic and occluded objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-frame depth estimation using cost volumes is employed, then depth estimation accuracy for static objects is improved, but accuracy for dynamic objects, low texture areas, and occluded objects deteriorates
Solution Approach 1:
The system dynamically switches between single-frame and multi-frame depth estimation modes based on object characteristics. For dynamic objects, low texture areas, and occluded regions, it activates single-frame estimation to complement the multi-frame cost volume approach, ensuring reliable depth estimates across all object types regardless of motion or texture conditions
Solution Approach 2:
The system applies different depth estimation strategies to different regions of the image based on local characteristics. Single-frame features are specifically applied to dynamic objects, textureless regions, and occluded areas, while multi-frame cost volumes are used for static, well-textured regions, creating a spatially adaptive depth estimation system
2Measurement precision
If multi-frame depth estimation is used, then geometric relationships between images are leveraged, but complexity of the depth estimation network increases
Solution Approach 1:
Instead of processing all frames through the full multi-frame cost volume pipeline, the system selectively applies single-frame features only where needed (dynamic objects, low texture areas, occluded regions). This partial application reduces overall computational complexity while maintaining accuracy where it matters most
Solution Approach 2:
The depth estimation problem is segmented into two independent components: multi-frame cost volume estimation for static regions and single-frame estimation for dynamic regions. This segmentation allows each component to be optimized independently, reducing the overall network complexity compared to a unified approach that must handle all cases
Data Source
AI summary
A method for controlling a vehicle in an environment includes generating, via a cross-attention model, a cross-attention cost volume based on a current image of the environment and a previous image of the environment in a sequence of images. The method also includes generating combined features by combining cost volume features of the cross-attention cost volume with single-frame features associated with the current image. The single-frame features may be generated via a single-frame encoding model. The method further includes generating a depth estimate of the current image based on the combined features. The method still further includes controlling an action of the vehicle based on the depth estimate.