3D Object Instance Mapping Using Video, Depth, and Pose Graphs
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Solution Overview
Problem
Current methods for constructing 3D space representations in computer vision and robotics face challenges in generating real-time, semantically meaningful maps of objects in a three-dimensional environment, often resulting in false detections, misclassifications, and inefficiencies due to the separation of geometric and semantic mapping approaches.
Innovation Solution
A method that applies an object recognition pipeline to video data frames, fusing mask outputs with depth data to generate a map of object instances using surface-distance metric values within a 3D object volume, and a pose graph for tracking and updating object poses, enabling real-time object detection and mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate 2D image classification and 3D scene mapping approaches are used, then existing technology can be maintained, but real-time semantic mapping accuracy deteriorates
Solution Approach 1:
The patent combines 2D image classification and 3D scene mapping into a unified object-centric mapping system that processes both geometric and semantic information simultaneously. The system integrates object detection, pose estimation, and 3D reconstruction in a single pipeline that operates in real-time, eliminating the performance degradation caused by separate processing approaches.
Solution Approach 2:
The mapping system is designed to perform multiple functions simultaneously: it constructs 3D geometric representations, identifies object instances, estimates poses, and generates semantic annotations all within a single unified framework. This multi-functional approach maintains real-time performance while improving overall mapping accuracy.
2Measurement precision
If complex object detection pipelines are used, then detection accuracy improves, but processing speed deteriorates
Solution Approach 1:
The system extracts and processes only the essential features needed for object detection and mapping, rather than analyzing all image data. By focusing computation on relevant object regions and using efficient feature extraction techniques, the system maintains high detection accuracy while reducing processing time to meet real-time requirements.
3Manufacturing precision
If detailed 3D representations are generated, then mapping precision improves, but computational complexity increases
Solution Approach 1:
The system generates detailed 3D representations selectively for detected object instances rather than for the entire environment. Each object receives focused computational resources for precise modeling, while background and non-critical regions use simplified representations, thereby maintaining high mapping precision for important objects without overwhelming computational complexity.
Data Source
AI summary
A method comprising applying an object recognition pipeline to frames of video data. The object recognition pipeline provides a mask output of objects detected in the frames. The method includes fusing the mask output of the object recognition pipeline with depth data associated with the frames of video data to generate a map of object instances, including projecting the mask output to a model space for the map of object instances using a camera pose estimate and the depth data. An object instance in the map of object instances is defined using surface-distance metric values within a three-dimensional object volume, and has an object pose estimate indicating a transformation of the object instance to the model space. The object pose estimate and the camera pose estimate form nodes of a pose graph for the map of model instances.