3D Point Cloud Pipeline for Fusing Depth and Video Data
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Solution Overview
Problem
Conventional multimedia and AI pipelines lack support for 3D data processing, relying on 2D-based image processing and deep learning, which limits their ability to efficiently handle 3D point clouds and depth data.
Innovation Solution
A 3D data processing pipeline is developed that integrates with video analysis applications, allowing for the processing of 3D point cloud data within multimedia frameworks. This pipeline combines 3D depth and range data with 2D camera image data, enabling more powerful, accurate, and precise perception and vision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional 2D-based image processing pipelines are used, then the system is simple and compatible with existing frameworks, but the system cannot effectively process 3D point cloud data and depth information
Solution Approach 1:
The patent extends conventional 2D image processing pipelines into 3D space by introducing depth buffers, point cloud data structures, and volumetric processing capabilities. This allows the system to handle 3D point cloud data while building upon existing 2D processing infrastructure, effectively adding a dimensional layer rather than completely replacing the pipeline.
Solution Approach 2:
The processing pipeline is designed to handle multiple data types simultaneously - 2D images, depth maps, and 3D point clouds - using a unified framework. This multi-functional approach allows the same pipeline to process various data formats without requiring separate dedicated systems for each type.
2Measurement precision
If depth data is merged into color frames as RGBD format, then the data can be processed using existing 2D frameworks, but depth accuracy is lost due to format limitations
Solution Approach 1:
The patent separates depth data from color data, maintaining them as distinct data structures rather than merging them into a single RGBD format. This segmentation preserves the full precision of depth information while allowing independent processing of color and depth channels, avoiding the accuracy loss inherent in combined formats.
Solution Approach 2:
The patent introduces intermediate data structures such as depth buffers and point cloud representations that act as mediators between raw sensor data and final processing. These intermediaries preserve depth accuracy during transmission and processing, allowing high-precision depth data to flow through the pipeline without being constrained by format limitations.
3Adaptability or versatility
If conventional solutions with limited frame buffers are used, then the system is easier to implement, but the system lacks scalability for custom 3D data processing requirements
Solution Approach 1:
The patent implements dynamic frame buffers that can be configured and resized based on specific application requirements. Rather than fixed-size buffers, the system allows runtime allocation and configuration of buffer dimensions, enabling customization for different 3D data processing scenarios while maintaining a relatively simple base implementation.
Solution Approach 2:
The system allows modification of key parameters such as buffer sizes, data types, and processing resolutions to adapt to different 3D processing needs. This parameter-based customization enables the same core pipeline to be adapted for various applications without requiring complete redesign, balancing simplicity with flexibility.
Data Source
AI summary
In various examples, a three-dimensional (3D) data processing pipeline for autonomous systems and applications is presented. Systems and methods are disclosed for 3D point cloud data processing fused with video analysis applications. Using the systems and methods described herein, processing of 3D data may be performed in different multimedia frameworks, allowing a user to use common libraries and/or to implement custom libraries on top of the existing system design. As a result, conventional 2D video processing may be combined with 3D data processing, to allow for data representing a flat 2D world to represent a rich 3D world. In this way, the fused 3D depth and/or range data with 2D camera image data allows for perception and/or vision that is more powerful, accurate, and precise.