Single-Frame Monocular Camera Depth Calibration Using Learned Scaling
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Solution Overview
Problem
Monocular depth estimation (MDE) systems face challenges such as scale ambiguity and performance shortfalls in edge-rich regions, particularly in dynamic scenes, due to the limitations of self-supervised learning and resource constraints, which hinder their application in autonomous vehicles and mobile robotics.
Innovation Solution
A method involving a multi-frame absolute depth teacher model and knowledge distillation is used to train a single-frame student model, enabling it to estimate metric depth by learning a scaling factor, incorporating panoptic segmentation and multi-scale feature fusion to enhance depth estimation accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If self-supervised learning is used for monocular depth estimation, then training cost and labor are reduced, but scale ambiguity is introduced
Solution Approach 1:
The patent introduces a scaling factor network as an intermediary component that bridges the gap between scale-invariant depth predictions and metric depth estimates. This network learns to predict appropriate scaling factors from image features, enabling the conversion of relative depth maps to absolute metric depth without requiring annotated ground truth data, thus resolving the scale ambiguity issue while maintaining self-supervised training benefits
Solution Approach 2:
The patent changes the parameter representation by introducing learnable scaling factors that transform scale-invariant depth predictions into metric depth estimates. By parameterizing the relationship between different depth scales through learnable factors derived from image content, the system achieves accurate metric depth estimation while maintaining the computational efficiency and data efficiency of self-supervised learning
2Adaptability or versatility
If photometric loss is used for self-supervised depth estimation, then scalability to novel scenes is achieved, but scale ambiguity and training instability occur
Solution Approach 1:
The scaling factor network serves as a mediator that decouples the depth estimation from direct photometric loss optimization. Instead of directly optimizing for photometric accuracy which causes scale ambiguity, the system optimizes for scale-invariant depth predictions and then applies learned scaling factors to obtain metric depth, thereby achieving both scalability and accuracy
Solution Approach 2:
The patent segments the depth estimation task into two independent parts: (1) predicting scale-invariant depth using self-supervised learning with photometric loss, and (2) learning scaling factors from image features to convert to metric depth. This segmentation allows each component to be optimized independently, with the first part benefiting from scalability and the second part resolving scale ambiguity
3Measurement precision
If multi-frame absolute depth estimation is used, then depth accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent creates a simplified single-frame student model that copies the architectural structure of the complex multi-frame teacher model but operates on compressed feature representations. The student model learns to predict scaling factors directly from single-frame image features, effectively copying the teacher's depth estimation capability while reducing computational complexity by eliminating the need for multi-frame processing
Solution Approach 2:
The patent applies partial action by focusing the student model on predicting only the scaling factor rather than computing full metric depth maps from scratch. This partial prediction approach reduces computational complexity while maintaining accuracy, as the scaling factor contains the essential information needed to transform scale-invariant depth to metric depth
4Productivity
If single-frame monocular depth estimation is used, then real-time performance is achieved, but depth accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-training the scaling factor network using knowledge distillation from a multi-frame teacher model. This preliminary training enables the student model to capture complex depth relationships learned from multi-frame data, allowing it to achieve high accuracy in single-frame inference without requiring real-time multi-frame processing
Solution Approach 2:
The single-frame student model copies the architectural framework of the multi-frame teacher model but operates with compressed features and scaled predictions. By copying the teacher's learned representations and applying them through the scaling factor network, the student model achieves real-time performance while maintaining the accuracy insights gained from multi-frame analysis
Data Source
AI summary
A system and method are provided for estimating a depth of an object within an image and training a depth estimation network. The depth estimation method includes: obtaining single-frame image data; obtaining scaling factor data based on the single-frame image data; generating scale-invariant depth data through inputting the single-frame image data into a depth estimation network; and generating metric depth data based on the scaling factor data and the scale-invariant depth data. The training method includes: inputting image data into a teacher machine learning (ML) model in order to generate metric depth data; inputting image data into a student ML model in order to generate scale-invariant depth data; and training a student network based on loss calculated using the metric depth data and the scale-invariant depth data.