Multi-Camera Depth Estimation With Scale-Aware Projection Loss
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Solution Overview
Problem
Current self-supervised depth and ego-motion models are limited by their scale-agnostic photometric loss, which prevents them from generating metrically accurate models, making them unsuitable for downstream tasks requiring metric scale information, such as 3D object detection, and are restricted to stereo settings with high camera overlap, increasing costs and complexity.
Innovation Solution
The approach introduces a multi-camera system that leverages known extrinsics and small overlaps between cameras to generate scale-aware depth and ego-motion models using a derived photometric loss, enabling learning of scale in a fully self-supervised manner and reducing the number of cameras required for full 360° coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If scale-agnostic photometric loss is used for training depth and ego-motion models, then the models can be trained in a self-supervised manner, but the models cannot generate metrically accurate depth estimates
Solution Approach 1:
The patent modifies the photometric loss function by incorporating scale-aware constraints that leverage known camera extrinsics. This parameter change transforms the traditional scale-agnostic photometric loss into a scale-aware variant that can learn metric depth while maintaining self-supervised training capability. The loss function now includes terms that enforce consistency with known camera geometry, enabling the model to recover absolute scale information from image sequences.
2Measurement precision
If stereo settings with high camera overlap are used, then depth estimation can be performed, but the system cost and complexity increase
Solution Approach 1:
The patent extracts and leverages the known extrinsic parameters between cameras as a separate piece of information that can be obtained through calibration. By taking out the scale information from the image data and encoding it in the loss function through known extrinsics, the system can use simpler camera configurations with minimal overlap, reducing hardware complexity while maintaining depth estimation capability.
3Device complexity
If multiple cameras with minimal overlap are used, then system cost is reduced, but scale-aware depth estimation becomes difficult
Solution Approach 1:
The patent introduces the known camera extrinsics as an intermediary that bridges the gap between minimal camera overlap and scale-aware depth estimation. The extrinsic parameters serve as a mediator that encodes the geometric relationship between cameras, allowing the loss function to enforce scale consistency even when camera views have minimal overlap. This intermediary enables scale-aware learning without requiring complex camera configurations.
Data Source
AI summary
A method for scale-aware depth estimation using multi-camera projection loss is described. The method includes determining a multi-camera photometric loss associated with a multi-camera rig of an ego vehicle. The method also includes training a scale-aware depth estimation model and an ego-motion estimation model according to the multi-camera photometric loss. The method further includes predicting a 360° point cloud of a scene surrounding the ego vehicle according to the scale-aware depth estimation model and the ego-motion estimation model. The method also includes planning a vehicle control action of the ego vehicle according to the 360° point cloud of the scene surrounding the ego vehicle.