3D Object Detection Using Depth Cameras and 2D Models
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Solution Overview
Problem
Current 3D object detection algorithms for autonomous driving require a large number of labeled samples, struggle with accurately detecting object rotation angles, and rely on expensive lidars or radars with limited field of view for depth information.
Innovation Solution
A method for detecting three-dimensional objects in autonomous driving systems using a combination of detection images and depth images, where the electronic device inputs detection images into a trained object detection model to determine object categories and 2D bounding boxes, and then calculates 3D bounding boxes and rotation angles using depth images and a 3D object model library.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If lidars or radars are used to obtain depth information, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent combines 2D object detection with depth estimation in a unified framework. The 2D detection model and depth estimation network are integrated to process images simultaneously, producing both object categories and depth information in one pass rather than using separate expensive sensors like lidars or radars.
Solution Approach 2:
The patent uses 2D image data to create a virtual depth representation through learned features. Instead of directly measuring depth with physical sensors, the system learns to infer depth from 2D image features using a depth estimation network, creating a virtual depth map that replaces the need for expensive depth-sensing hardware.
2Measurement precision
If rotation angle detection is performed using current 3D target detection algorithms, then measurement precision is improved, but loss of time increases due to regression operations
Solution Approach 1:
The patent pre-processes images to extract 2D bounding boxes and object categories before performing 3D detection. By obtaining accurate 2D detection results first, the system establishes a solid foundation that reduces the computational burden and time required for subsequent 3D rotation angle estimation, as the preliminary 2D information constrains the search space for 3D parameters.
3Measurement precision
If a large number of labeled samples are used for training, then measurement precision is improved, but loss of time increases during the training process
Solution Approach 1:
The patent segments the detection task into two parts: 2D object detection (categories and bounding boxes) and 3D depth estimation. This segmentation allows the system to use extensively labeled 2D detection data which is abundant and easy to obtain, while the 3D depth estimation can be trained with fewer samples since it builds upon the already-detected 2D information. The segmentation enables leveraging existing 2D labeled data without requiring expensive 3D annotated datasets.
Data Source
AI summary
A method for detecting three-dimensional (3D) objects in relation to autonomous driving is applied in an electronic device. The device obtains detection images and depth images, =inputs the detection images into a trained object detection model to determine categories of objects in the detection images and two-dimensional (2D) bounding boxes of the objects. The device determines object models of the objects and 3D bounding boxes of the object models according to the object categories, and calculates point cloud data of the objects selected and distances from the depth camera to the object models. The device determines angles of rotation of the object models of the objects according to the object models of the objects and the point cloud data, and can determine respective positions of the objects in 3D space according to the distance from the depth camera to the object models, the rotation angles, and the 3D bounding boxes.