Self-Supervised Monocular Depth Estimation Using Adaptive Cost Volume
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Solution Overview
Problem
Current monocular depth estimation methods often fail to utilize sequence information from video frames effectively, leading to suboptimal depth predictions and requiring costly detection and ranging systems for ground truth data, which are also ineffective for reflective objects.
Innovation Solution
A self-supervised monocular depth estimation model is trained using a time series of images, employing an adaptive cost volume and a secondary depth estimation network to reduce overfitting and leverage geometric information from multiple frames, allowing depth map generation from a single input image with optional supplementary frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detection and ranging systems (RADAR, LIDAR) are used to establish ground truth depth, then depth measurement precision is improved, but device complexity and cost increase significantly
Solution Approach 1:
The patent uses video frames (optical copies) from monocular cameras to replace expensive detection and ranging systems. By copying temporal sequences from inexpensive monocular video, the system creates supervision signals that substitute for ground truth depth data from complex systems like LIDAR or RADAR, achieving depth estimation without requiring those complex devices.
Solution Approach 2:
The patent replaces mechanical/optical detection systems (RADAR, LIDAR) with a computational approach using neural networks. Instead of using physical systems that emit and detect waves to measure depth, the invention uses software-based monocular depth estimation networks that process video frames to infer depth information, substituting mechanical measurement systems with computational models.
2Measurement precision
If detection and ranging systems are used to sense depth, then depth measurement precision is improved, but loss of time and resources increases due to constant utilization requirements
Solution Approach 1:
The patent copies temporal information from monocular video frames to create supervision signals for training. Instead of continuously operating expensive detection and ranging systems to capture ground truth depth, the system copies sequences from inexpensive monocular video recordings, significantly reducing time and resource consumption while maintaining training effectiveness.
3Measurement precision
If detection and ranging systems are used for depth sensing, then depth measurement precision is improved, but reliability decreases for reflective objects that render them undetectable
Solution Approach 1:
The patent replaces detection and ranging systems with a neural network-based monocular depth estimation approach. This substitution eliminates the fundamental limitation of reflective objects undetectable by RADAR/LIDAR, as the neural network infers depth from visual appearance and temporal sequences in monocular video, which can capture reflective surfaces that traditional ranging systems miss.
Solution Approach 2:
The system uses self-supervised learning where the monocular depth estimation network trains itself using temporal consistency from video sequences. The network learns to predict depth by enforcing that predicted depths remain consistent across video frames, allowing it to improve reliability for challenging objects like reflective surfaces without requiring external ground truth from detection and ranging systems.
4Measurement precision
If sequence information from video frames is utilized for depth estimation, then depth prediction accuracy is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The patent implements self-supervised learning where the depth estimation network uses its own predictions across video frames as feedback. By enforcing temporal consistency constraints—where predicted depths should remain stable across frames for static objects—the system creates internal feedback loops that improve accuracy without requiring external sensors or complex additional processing hardware.
Data Source
AI summary
A multi-frame depth estimation model is disclosed. The model is trained and configured to receive an input image and an additional image. The model outputs a depth map for the input image based on the input image and the additional image. The model may extract a feature map for the input image and an additional feature map for the additional image. For each of a plurality of depth planes, the model warps the feature map to the depth plane based on relative pose between the input image and the additional image, the depth plane, and camera intrinsics. The model builds a cost volume from the warped feature maps for the plurality of depth planes. A decoder of the model inputs the cost volume and the input image to output the depth map.