Monocular Depth Estimation via Temporal Probability Volume
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Solution Overview
Problem
Current methods for estimating depth from a single monocular image suffer from low accuracy, instability, and domain specificity, with active techniques facing limitations such as restricted operating ranges and high power consumption, while deep-learning based techniques provide inadequate results when applied to video streams.
Innovation Solution
A system and method using a deep-learning neural network to generate a depth probability distribution volume (DPV) that associates pixels with a set of depth candidates and confidence values, incorporating a sliding window approach to process a sequence of image frames, warping features, and applying softmax functions to stabilize depth estimation over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If basic deep-learning based techniques are used for estimating depth from a single monocular image, then the system complexity is reduced, but the accuracy and temporal stability of depth estimation deteriorate
Solution Approach 1:
The patent combines multiple monocular images captured at different time points into a unified depth estimation process. By integrating temporal information from a sequence of images through a sliding window approach, the system achieves higher accuracy and temporal stability without requiring complex active sensors or stereo camera setups.
Solution Approach 2:
The patent implements a dynamic sliding window mechanism that adapts to temporal changes in the scene. The window size and composition are adjusted based on the specific application requirements, allowing the system to optimize between temporal stability and responsiveness to scene changes, thereby improving depth estimation accuracy dynamically.
2Measurement precision
If deep-learning neural network is trained for a specific domain, then the measurement precision for that domain is improved, but the adaptability to multiple domains deteriorates
Solution Approach 1:
The patent develops a universal depth estimation framework that can be applied across multiple domains (indoor, outdoor, various lighting conditions) without requiring domain-specific training. The method uses generic monocular image sequences and produces consistent results across different environments, eliminating the need for separate trained models for each domain.
3Measurement precision
If active sensors are used to measure depth information, then the measurement precision is improved, but the power consumption and device complexity increase
Solution Approach 1:
The patent enables the monocular camera system to extract depth information inherently present in the image sequence without requiring additional active sensors. By utilizing temporal variations and optical flow information from standard RGB camera frames, the system achieves depth estimation without the high power consumption associated with active sensing technologies.
4Measurement precision
If active sensors are used to capture depth information, then the measurement precision is improved, but the device complexity and setup requirements increase
Solution Approach 1:
The patent extracts depth information from the temporal sequence of monocular images without requiring additional depth sensors. By removing the need for active sensors and focusing on processing standard image sequences, the system simplifies the hardware setup while maintaining depth measurement capabilities through computational methods.
Data Source
AI summary
Techniques for estimating depth for a video stream captured by a monocular image sensor are disclosed. A sequence of image frames are captured by the monocular image sensor. A first neural network is configured to process at least a portion of the sequence of image frames to generate a depth probability volume. The depth probability volume includes a plurality of probability maps corresponding to a number of discrete depth candidate locations over a range of depths defined for the scene. The depth probability volume can be updated using a second neural network that is configured to generate adaptive gain parameters to integrate the DPVs over time. A third neural network is configured to refine the updated depth probability volume from a lower resolution to a higher resolution that matches the original resolution of the sequence of image frames. A depth map can be calculated based on the depth probability volume.