Sparse Auxiliary Network for Monocular Depth Completion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for determining depth from monocular images face inaccuracies, while LiDAR-based approaches encounter high computational loads and issues with depth completion due to sparse data, making reliable depth data derivation challenging.
Innovation Solution
A novel network architecture that integrates monocular images with sparse depth data using a sparse auxiliary network (SAN) to enhance depth prediction and completion, allowing for robust depth estimation even without explicit depth data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If monocular images are used to derive depth information, then the approach avoids expensive LiDAR sensors, but depth inaccuracies and aberrations occur
Solution Approach 1:
The patent combines monocular image data with sparse depth data from LiDAR sensors into a unified depth completion framework. The neural network integrates features from both modalities, allowing the system to leverage the cost-effectiveness of monocular cameras while correcting their depth inaccuracies using sparse LiDAR measurements, thus resolving the contradiction between cost and depth accuracy.
Solution Approach 2:
The patent introduces a sparse auxiliary network (SAN) as an intermediary component that processes sparse LiDAR depth data and injects it into the main encoder-decoder architecture via skip connections. This intermediary structure enables the seamless integration of sparse depth information to guide and correct monocular depth predictions, improving measurement precision without requiring dense LiDAR data.
2Measurement precision
If LiDAR data is used to provide depth information, then explicit depth data is obtained, but high computational loads and issues with depth completion occur due to sparse data
Solution Approach 1:
The patent applies partial action by using only sparse LiDAR depth data rather than processing complete dense depth maps. The sparse auxiliary network processes only the necessary sparse measurements and selectively injects relevant depth features into the main network at appropriate spatial dimensions, reducing computational load while maintaining depth accuracy where LiDAR data is available.
Solution Approach 2:
The patent segments the depth estimation task into two independent processing streams: a main encoder-decoder network that processes monocular images, and a separate sparse auxiliary network that processes LiDAR data. This segmentation allows each stream to be optimized independently, reducing overall computational complexity while enabling effective fusion of both data sources through skip connections.
3Device complexity
If a depth model uses a single input stream, then the architecture is simpler, but robustness decreases when sensors encounter difficulties or fail
Solution Approach 1:
The patent creates a multi-functional depth estimation system where the main encoder-decoder network can process monocular images independently, while the sparse auxiliary network provides an additional functional path for LiDAR data. This universal architecture allows the system to adapt to different sensor configurations and failure modes, maintaining robustness whether LiDAR is available or not, thus resolving the contradiction between simplicity and reliability.
4Measurement precision
If sparse depth data is integrated into the depth model, then depth estimation accuracy improves, but the device complexity increases
Solution Approach 1:
The sparse auxiliary network serves as an intermediary structure that simplifies the integration of sparse depth data. By providing a dedicated processing path with skip connections that inject depth features at matching spatial dimensions, the intermediary SAN reduces the complexity of fusing sparse and dense data streams while improving depth estimation accuracy through guided feature integration.
Data Source
AI summary
System, methods, and other embodiments described herein relate to determining depths of a scene from a monocular image. In one embodiment, a method includes generating depth features from sensor data according to whether the sensor data includes sparse depth data. The method includes selectively injecting the depth features into a depth model. The method includes generating a depth map from at least a monocular image using the depth model that is guided by the depth features when injected. The method includes providing the depth map as depth estimates of objects represented in the monocular image.