Bird's-Eye Object Identification Through Explicit Depth Co-Training
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Solution Overview
Problem
Existing autonomous vehicle systems face challenges in accurately and efficiently detecting and classifying objects from camera images due to perspective distortions and the need for precise depth estimation, which is often reliant on costly and maintenance-intensive lidar sensors.
Innovation Solution
A system utilizing neural networks trained with depth ground truth data to estimate pixel depths and project images to a bird's-eye view, enabling efficient object detection and classification using camera images alone, supplemented by lidar data during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidar sensors are used for depth estimation, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a teacher model trained with lidar depth data to generate pseudo-depth labels, which are then used to train a student model that processes only camera images. This copying approach transfers the depth estimation capability from the complex lidar-based teacher system to a simpler camera-based student system, maintaining measurement precision while reducing device complexity.
Solution Approach 2:
The patent introduces an intermediary teacher-student training framework where the teacher model acts as a mediator to transfer depth estimation knowledge to the student model. This intermediary mechanism enables the student model to achieve accurate depth estimation without direct access to lidar sensors, resolving the contradiction between precision and complexity.
2Reliability
If traditional object detection methods are used, then reliability is improved, but productivity decreases due to computational overhead
Solution Approach 1:
The patent performs preliminary depth estimation and bird's-eye view projection before object detection, transforming the input data into a more suitable representation. This preliminary action of projecting images to bird's-eye view using estimated depth values prepares the data in advance, making the subsequent object detection faster and more accurate without requiring heavy computational resources during real-time processing.
3Ease of operation
If perspective camera images are used directly, then ease of operation is improved, but measurement precision deteriorates due to perspective distortions
Solution Approach 1:
The patent transforms 2D perspective camera images into a bird's-eye view representation by projecting pixels along depth rays. This dimensional transformation eliminates perspective distortions while maintaining the simplicity of camera-based operation, as the system continues to use only camera inputs but processes them in a geometrically corrected bird's-eye view space for improved measurement precision.
Data Source
AI summary
The described aspects and implementations enable efficient detection and classification of objects with machine learning models that deploy a bird's-eye view representation and are trained using depth ground truth data. In one implementation, disclosed are system and techniques that include obtaining images, generating, using a first neural network (NN), feature vectors (FVs) and depth distributions pixels of images, wherein the first NN is trained using training images and a depth ground truth data for the training images. The techniques further include obtaining a feature tensor (FT) in view of the FVs and the depth distributions, and processing the obtained FTs, using a second NN, to identify one or more objects depicted in the images.