Helping the new generation of AI perception era: Biaobei Technology innovates 3D point cloud segmentation technology

3D point cloud annotation refers to annotating point data collected from three-dimensional objects to facilitate the training and application of computer vision algorithms. Compared with 2D images, 3D point clouds can provide rich geometric shape and scale information, and are not easily affected by changes in light intensity and occlusion by other objects, allowing us to better perceive and understand three-dimensional space.

For example, in the field of autonomous driving, 3D point cloud annotation technology can provide more accurate and reliable vehicle perception and obstacle avoidance support, helping vehicles achieve safer and smarter driving; in medical image analysis, Accurate segmentation and labeling of 3D point cloud data in medical images can help doctors make more accurate disease diagnoses and formulate treatment plans; in the field of robotics, 3D point cloud labeling can assist robots in achieving precise positioning and navigation. and obstacle avoidance to improve the intelligence of the robot

With the support of the Internet of Things, digital twins, artificial intelligence and other technologies, 3D point cloud has become an important technical foundation for the development of the "intelligent world", and the demand for 3D point cloud annotation is also growing

What is point cloud segmentation?

The so-called point cloud refers to a set of points. As an advanced three-dimensional data representation method, 3D point cloud can transform objects or scenes in space into a data set composed of a large number of discrete points. Each point contains spatial coordinate information and other possible attributes

Unlike 2D image data structures with regular pixel layout, 3D point cloud data are unordered and irregular, making it difficult to infer the underlying information. This requires an important technology in 3D point cloud processing - 3D point cloud segmentation tool to segment each object in the point cloud scene to obtain accurate object information, environmental conditions and other data.

Simply put, 3D point cloud segmentation refers to classifying and labeling objects in point cloud data, and ultimately realizing the identification and positioning of objects in the environment, which is the key to scene understanding. However, due to the sparseness, irregularity, and large amount of calculation of 3D point cloud data, it also brings great challenges to segmentation and annotation.

Biaobei Technology 3D Point Cloud Segmentation

Biaobei Technology has developed a set of leading solutions in point cloud annotation and modeling based on years of accumulation of artificial intelligence technology and business experience. This solution uses large-scale models for automated annotation, which is efficient, accurate, and automated. At the same time, it also integrates a variety of scene annotation tools, which can convert point cloud data into highly restored 3D models, providing strong technical support for the development of various fields

In response to the problem of point cloud segmentation and labeling, Biaobei Technology relies on high-precision visual models to provide comprehensive and intelligent 3D point cloud segmentation tools, including rectangles, circles, polygons, clicks, brushes, etc., to fully Parse and display 3D point cloud data, color and segment different objects to be annotated, and assign semantic labels, such as vehicles, pedestrians, trees, etc., to better explain real-life three-dimensional scenes.

Currently, Biaobei Technology’s 3D point cloud platform adopts a variety of sensor fusion methods, which can support multiple data types such as laser radar, cameras, millimeter wave radar, and camera position maps, and align and fuse these data through algorithms. , to provide a more accurate and unified view.

01 Continuous frame track drawing

In the traditional frame-by-frame annotation mode, annotators usually copy objects manually, which takes a lot of time. In order to solve this problem, Biaobei Technology introduced an algorithm-assisted method. It uses a pre-processed model to extract features and identify objects that need to be annotated. Then, it performs target association matching on the same objects, and uses algorithms such as model tracking to mark the same trackID in thousands of frames of data. In this way, the same object can be tracked and marked, maintaining the consistency of data annotation, while greatly saving the annotator's time to adjust the object by copying and supplementing

The content that needs to be rewritten is: 02 Frame annotation

In actual projects, point cloud data is usually unstructured data, or there are problems such as data truncation and changes in occlusion angles collected at different frames, resulting in subjective deviations of annotators affecting annotation accuracy and efficiency. Biaobei Technology's 3D point cloud platform supports overlay and fusion annotation of consecutive frames. Label the same object in different frames with a unified ID to build an overall point cloud model of the scene, providing richer semantic information, such as object type, motion trajectory, speed, etc., to facilitate subsequent processing and analysis.

03 Intelligent Tools

In addition, Biaobei Technology uses advanced algorithms to provide a variety of intelligent auxiliary tools, which significantly improves the efficiency of 3D point cloud segmentation. For example, the annotator only needs to simply select a single result or select a range, and the platform can quickly identify the objects within the range, automatically perform segmentation and annotation, and return the final results to the annotator. The annotator only needs to make simple modifications or adjustments based on the pre-annotated results to complete the annotation work

In order to improve the efficiency and accuracy of point cloud segmentation, Biaobei Technology also uses the powerful model capabilities of artificial intelligence to build a machine vision algorithm into the platform. By adjusting the parameters of the model, objects such as lane lines and curbs can be identified with high quality, thereby greatly reducing manual duplication of work and improving annotation efficiency

Currently, Biaobei Technology’s 3D point cloud platform supports point cloud panoramic segmentation and point cloud instance segmentation. By accurately segmenting and processing point cloud data, more accurate and refined object models and scene information can be obtained, providing high-quality training data for machine learning and artificial intelligence algorithms. The platform is suitable for robot navigation, autonomous driving, 3D reconstruction, virtual reality and other fields

With the continuous advancement of technology and the continuous expansion of application scenarios, 3D point cloud segmentation and annotation technology provides more accurate and efficient 3D data processing and analysis methods for various fields, and has great value. Biaobei Technology will create more meaningful and valuable data products supported by its strong data service capabilities, and provide strong support for the development of artificial intelligence and machine learning

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