3D Object Detection from 2D Images via Depth Dataset
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Solution Overview
Problem
Current methods for object detection in two-dimensional images, such as those used in real-time vehicle detection, are limited in their ability to extract three-dimensional features from two-dimensional images, which is essential for accurate object representation and application in fields like self-driving vehicles.
Innovation Solution
A method and system that input a two-dimensional image into an object detection model to generate a detection depth dataset, which includes a 2D bounding box and depth markings, allowing for the calculation of points-of-interest in a 3D camera-centered coordinate system and subsequent conversion into a 3D global coordinate system, effectively transforming 2D features into 3D representations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection methods using CNN are used to obtain object features in two-dimensional images, then object detection can be performed, but three-dimensional features of the object cannot be obtained
Solution Approach 1:
The patent transforms 2D image data into 3D spatial information by introducing depth estimation and coordinate transformation. The object detection model outputs not only 2D bounding boxes but also depth values and orientation angles, which are then converted into 3D camera-centered coordinates and finally into 3D global coordinates, effectively adding a third dimension to the detection output.
Solution Approach 2:
The patent introduces an intermediary coordinate transformation process that converts 2D image coordinates into 3D camera-centered coordinates using depth information and rotation angles. This intermediary step acts as a bridge between 2D image data and 3D spatial representation, enabling the recovery of three-dimensional features from two-dimensional images.
2Reliability
If only two-dimensional bounding boxes are used for object detection, then detection speed is maintained, but accurate three-dimensional representation and geographic location information are lost
Solution Approach 1:
The patent segments the object detection output into multiple independent components: 2D bounding box coordinates, depth values, and orientation angles (azimuth and elevation). Each component is processed separately through coordinate transformation equations, allowing the system to maintain modularity while achieving comprehensive 3D representation.
Solution Approach 2:
The patent transforms the detection parameters from 2D image space to 3D physical space by introducing depth distance, azimuth angle, and elevation angle as additional parameters. These parameter changes enable the conversion of 2D bounding boxes into 3D spatial coordinates, providing accurate geographic location information while maintaining the original detection framework.
3Measurement precision
If three-dimensional features are extracted from two-dimensional images, then accurate object representation is achieved, but the detection process becomes more complex
Solution Approach 1:
The patent replaces complex mechanical 3D measurement systems with a computational approach. Instead of using multiple cameras or LIDAR sensors to directly capture 3D data, the system uses a single 2D image combined with neural network-based depth estimation and coordinate transformation algorithms to reconstruct 3D features, significantly simplifying the hardware requirements while maintaining measurement accuracy.
Data Source
AI summary
A method for detecting a three-dimensional object in a two-dimensional image includes: inputting the two-dimensional image into an object detection model, and obtaining a resulting detection depth dataset; obtaining, based on the detection depth dataset, coordinate sets of a number of points-of-interest each associated with a to-be-detected object in a 3D camera centered coordinate system; and converting the coordinate sets of the number of points-of-interest in the 3D camera centered coordinate system into a number of coordinate sets in a 3D global coordinate system. Embodiments of this disclosure may be utilized in the field of self-driving cars with roadside traffic cameras.