3D Object Labeling with 2D Guide Features for Faster Annotation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for labeling objects in image data, particularly for robotic control and autonomous vehicle navigation, require large-scale datasets with high-quality annotations, but manually annotating 3D sensor data is time-consuming and expensive, and associating 2D and 3D labels is complex and resource-intensive.
Innovation Solution
A system that generates guide features to assist users in localizing target objects in 3D frames using 2D image data, reducing the need for manual annotation and simplifying the association of 2D and 3D labels by displaying a guide feature overlay on 3D frames, allowing users to input 3D positions and orientations directly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of 3D sensor data is performed to achieve high-quality object labels, then labeling accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The system performs preliminary 2D object detection and generates candidate 3D labels automatically before manual annotation. This preliminary action pre-processes the data by identifying objects in 2D images and projecting them to 3D space, creating initial label candidates that reduce the scope of subsequent manual annotation work while maintaining high accuracy
Solution Approach 2:
The system introduces 2D image data as an intermediary to facilitate 3D label generation. By detecting objects in 2D images and using camera extrinsics to project these detections to 3D point clouds, the system creates an intermediate representation that bridges 2D visual data and 3D spatial labels, significantly reducing manual annotation requirements
2Measurement precision
If separate processing of 2D and 3D labels is performed to ensure data quality, then labeling precision is improved, but system complexity increases
Solution Approach 1:
The system merges 2D object detection results with 3D point cloud data by projecting 2D bounding boxes to 3D space using camera extrinsics. This merging process combines information from both modalities to generate unified 3D object labels, eliminating the need for separate processing pipelines while maintaining precision through multi-modal data fusion
Solution Approach 2:
The system creates a universal labeling approach that handles both 2D and 3D label generation through a single integrated process. The same pipeline that detects objects in 2D images also generates corresponding 3D labels by applying camera transformation matrices, making the system multi-functional without requiring separate specialized processing for each data type
Data Source
AI summary
Methods, computer systems, and apparatus, including computer programs encoded on computer storage media, for generating label data for one or more target objects in an environment. The system obtains first data characterizing the environment, wherein the first data includes position data characterizing a position of the target object. The system obtains second data including one or more three-dimensional (3D) frames characterizing the environment. The system determines, based on the first data, a guide feature for locating the target object in the 3D frames of the second data. The system receives a first user input that specifies at least an object position in the selected 3D frame, and generates label data for the target object based on the first user input.