3D Point-Cloud Labeling With VR Gestures for Precise Cuboids
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Solution Overview
Problem
Current methods for labeling 3D point-cloud data are tedious and time-consuming, particularly when using two-dimensional input devices, and fail to meet the precision requirements needed for accurate object labeling in complex environments, such as those encountered in self-driving vehicles.
Innovation Solution
A point-cloud frame labeling service that utilizes a Virtual Reality (VR) interface with hand gesture-based interaction, allowing users to create, edit, and delete 3D cuboid annotations using VR headsets and compatible input mechanisms, leveraging cloud provider networks for data processing and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If two-dimensional input devices (mouse and keyboard) are used for labeling 3D point-cloud data, then the labeling process becomes tedious and time-consuming, but the precision requirements for accurate object labeling in complex environments cannot be met
Solution Approach 1:
The patent transitions from 2D input devices to a 3D virtual reality environment for labeling point-cloud data. Users wear VR headsets and manipulate 3D cuboid annotations directly in three-dimensional space, matching the dimensional nature of the data being labeled. This dimensional alignment enables both faster labeling (improved productivity) and higher precision (improved measurement precision) simultaneously, resolving the technical contradiction.
2Ease of operation
If conventional 2D input devices are used, then device complexity remains low, but the ease of operation for creating precise 3D annotations deteriorates
Solution Approach 1:
The patent replaces traditional mechanical 2D input devices (mouse and keyboard) with a virtual reality system that uses hand gestures and spatial interaction. This substitution enables more intuitive and easier operation for creating 3D annotations, as users can directly manipulate virtual objects in 3D space rather than translating 2D inputs into 3D coordinates, significantly improving ease of operation despite increased system complexity.
3Quantity of substance
If manual labeling by human annotators is performed using traditional methods, then large quantities of data can be processed, but the time consumption and tedious nature of the task increase significantly
Solution Approach 1:
By implementing labeling in a 3D virtual reality environment rather than 2D, the system enables annotators to process large quantities of point-cloud data more efficiently. The immersive 3D interface allows for faster creation and manipulation of bounding box annotations, reducing the time required per annotation while maintaining the ability to handle large datasets, thus resolving the contradiction between data processing volume and time consumption.
Data Source
AI summary
Techniques for 3D point cloud labeling are described. An example of labeling includes initializing a point-cloud environment according to a configuration; loading of point-cloud data into a memory buffer of a device, wherein the point-cloud data is compatible with a virtual reality (VR) environment and a non-VR environment; drawing of at least the loaded point-cloud data into a point-cloud environment of the device; receiving user input in a task user interface of the device; and in response to the user input, performing an operation to one or more of a bounding box and quality of a label.