2-D Image Reconstruction in 3-D Simulation via Semantic Depth
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in accurately locating and presenting 2-D objects in 3-D simulations of environments, particularly when 2-D image data lacks depth and orientation information, making it difficult to accurately project objects into 3-D simulations created from combined 2-D and 3-D data.
Innovation Solution
The method involves using a semantic database of attributes to determine the average measurement, depth, and orientation of objects from 2-D image data, translating the coordinate system, and projecting a 3-D representation of the object into the 3-D simulation, with the aid of a neural network for improved object identification and placement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If 2-D image data is used to represent objects in 3-D simulations, then the simulation can incorporate real-world visual information, but the depth and orientation information is lost making accurate localization difficult
Solution Approach 1:
The patent transforms 2-D image data into 3-D representations by inferring depth and orientation dimensions. The system uses the semantic database to provide depth estimates and orientation angles for objects detected in 2-D images, thereby reconstructing three-dimensional spatial information from two-dimensional observations.
Solution Approach 2:
The semantic database serves as an intermediary between 2-D image data and 3-D simulation requirements. It provides bridge information including average measurements, depth estimates, and orientation angles that enable the system to accurately locate and represent objects in the 3-D simulation environment.
2Manufacturing precision
If semantic database attributes are used to determine object properties, then accurate 3-D representation can be achieved, but the system complexity increases
Solution Approach 1:
The semantic database is pre-populated with average measurements and typical attributes for various object categories before the simulation runs. This preliminary preparation allows the system to quickly query and retrieve appropriate 3-D representation parameters without complex real-time calculations.
Solution Approach 2:
The system dynamically adjusts object representation parameters based on queries to the semantic database. By changing parameters such as depth, orientation, and dimensions according to the detected object type and context, the system achieves accurate 3-D representations without requiring complex processing algorithms.
Data Source
AI summary
The present technology is directed to presenting a 3-D representation of an object, which is captured in a 2-D image of the object in an environment in a 3-D simulation of the environment. The present technology can receive 3-D data representing the environment including image data of the object in the environment, and a label identifying the object. The present technology can further locate a position of the object in the 3-D simulation of the environment based on determining a depth and an orientation of the object in the environment based on a semantic database of attributes associated with the object, obtain a 3-D representation of the object from the semantic database of attributes associated with the object, and project the 3-D representation of the object into the 3-D simulation of the environment at the determined position for the object.