Multi-View Depth Estimation Using Offline SfM Keyframe Selection
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Solution Overview
Problem
Existing 3D representation systems for autonomous agents, such as vehicles, face reduced accuracy in scene understanding and navigation due to the inclusion of images lacking depth information, which affects tasks like motion planning and obstacle avoidance.
Innovation Solution
A method for estimating depth using a system that selects keyframes from a sequence of images and identifies previously captured images with sufficient depth criteria, leveraging offline structure-from-motion for multi-view depth estimation, allowing for improved accuracy by incorporating images from different agents and times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all captured images are used for depth estimation, then more data is available for 3D representation, but images lacking depth information reduce the accuracy of scene understanding
Solution Approach 1:
The system extracts and selects only those images from the captured sequence that satisfy depth criteria (containing sufficient depth information), separating them from images that lack adequate depth data. This extraction process ensures that only high-quality images contribute to depth estimation, resolving the contradiction between using more images and maintaining accuracy.
Solution Approach 2:
The patent applies different quality standards to different images based on their depth information content. Instead of treating all images uniformly, the system evaluates each image's depth criteria satisfaction and selectively processes only those with high local quality (sufficient depth information), thereby maintaining overall system accuracy while utilizing multiple images.
2Device complexity
If only recently captured images are used for depth estimation, then computational complexity is reduced, but the accuracy of 3D representation deteriorates
Solution Approach 1:
The system performs preliminary evaluation of images to identify those satisfying depth criteria before the actual depth estimation process. By pre-selecting suitable images from the sequence based on depth information quality, the system reduces the computational load during real-time processing while ensuring that only high-quality images are used, thus maintaining accuracy without excessive complexity.
3Measurement precision
If images from multiple agents and time points are incorporated, then depth estimation accuracy improves, but system complexity increases
Solution Approach 1:
The depth estimation system is designed to universally process images from multiple sources (different agents and time points) through a unified framework. The same depth criteria evaluation and selection mechanisms apply regardless of the image source, allowing the system to leverage diverse image inputs for improved accuracy while managing complexity through consistent processing rules.
Data Source
AI summary
A method for estimating depth of a scene includes capturing a first image of the scene via one or more sensors associated with a first agent. The method also includes selecting one or more second images from a group of previously captured images of the scene, each second image of the one or more second images satisfying a depth criteria, each image of the group of previously captured images being captured prior to the first image. The method further includes estimating the depth of the scene based on the first image and the one or more second images.