Monocular Depth Estimation via Temporal Feature Accumulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing depth estimation methods in monocular imaging systems are unreliable due to their reliance on cost volume structures, which are computationally expensive and prone to errors, especially when they ignore temporal information.
Innovation Solution
The system generates a feature representation of a current image and updates accumulated feature information based on previous images and optical flow, using self-attention and cross-attention to estimate depth without requiring additional images or expensive computations, thereby improving depth estimation efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cost volume structures are used for depth estimation, then depth information can be obtained, but computational complexity increases and reliability decreases
Solution Approach 1:
The patent extracts and eliminates the cost volume structure from the depth estimation pipeline, replacing it with a transformer-based approach that directly processes image features and optical flow to compute depth, thereby reducing computational complexity while maintaining reliability
Solution Approach 2:
The patent substitutes the traditional mechanical/optical flow-based cost volume computation with a neural transformer model that uses self-attention and cross-attention mechanisms to compute depth estimates, replacing complex computational geometry operations with learned neural transformations
2Reliability
If traditional depth estimation methods are used, then depth information can be extracted, but temporal information is ignored leading to errors
Solution Approach 1:
The patent performs preliminary action by accumulating optical flow information and feature representations from multiple previous frames before processing the current frame, allowing the model to leverage temporal context and motion history to improve depth estimation accuracy
Solution Approach 2:
The patent maintains continuity of useful action by continuously updating and accumulating temporal features and optical flow information across multiple frames, ensuring that depth estimation benefits from ongoing temporal context rather than processing isolated frames
3Device complexity
If monocular imaging is used, then device simplicity is maintained, but depth estimation becomes unreliable
Solution Approach 1:
The patent applies universality by making the monocular imaging system multi-functional through the integration of transformer-based depth estimation, optical flow accumulation, and temporal feature processing, allowing a single camera to perform depth estimation with reliability previously requiring stereo vision systems
Data Source
AI summary
Disclosed are systems and techniques for capturing images (e.g., using a monocular image sensor) and detecting depth information. According to some aspects, a computing system or device can generate a feature representation of a current image and update accumulated feature information for storage in a memory based on a feature representation of a previous image and optical flow information of the previous image. The accumulated feature information can include accumulated image feature information associated with a plurality of previous images and accumulated optical flow information associated of the plurality of previous images. The computing system or device can obtain information associated with relative motion of the current image based on the accumulated feature information and the feature representation of the current image. The computing system or device can estimate depth information for the current image based on the information associated with the relative motion and the accumulated feature information.