Direct 3D Object Inference from Sensor Data
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Solution Overview
Problem
Conventional systems for autonomous agents require resource-intensive 2D to 3D object conversion, increasing detection times and system resource usage, whereas directly inferring 3D objects from sensor data is desirable for improved efficiency.
Innovation Solution
A method that extracts features from multiple sensors, encodes them into sensor space representations, reshapes these into a feature space representation of a spatial area, and projects the identified object's representation to control the autonomous vehicle's actions, bypassing the need for 2D to 3D conversion.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 2D to 3D object conversion is performed, then object detection accuracy is improved, but system resource usage increases and detection time increases
Solution Approach 1:
Instead of converting 2D bounding boxes to 3D objects, the patent inverts the approach by directly inferring 3D object locations and characteristics from sensor data without creating 2D bounding boxes first. This eliminates the conversion step entirely while maintaining detection accuracy.
Solution Approach 2:
The patent extracts and uses only the necessary 3D spatial information directly from sensor data, removing the unnecessary intermediate step of 2D bounding box creation and conversion. This extraction approach eliminates wasted computational resources on creating and then converting 2D representations.
2Measurement precision
If 2D to 3D object conversion is performed, then object detection accuracy is improved, but detection time increases
Solution Approach 1:
The patent inverts the conventional detection pipeline by directly inferring 3D object properties from sensor data without the intermediate 2D bounding box conversion step, thereby eliminating the time-consuming conversion process while maintaining detection accuracy.
Solution Approach 2:
The patent skips the unnecessary 2D to 3D conversion step entirely, rushing directly from sensor data to 3D object inference. This skipping of the intermediate conversion step significantly reduces detection time while preserving the necessary detection accuracy.
3Adaptability or versatility
If 2D to 3D object conversion is performed, then 3D object representation is obtained, but device complexity increases
Solution Approach 1:
The patent extracts directly the required 3D object representations from sensor data, removing the complex intermediate process of 2D bounding box creation and conversion. This extraction approach simplifies the system architecture while obtaining the necessary 3D representations.
Solution Approach 2:
The patent inverts the conventional approach by obtaining 3D object representations directly from sensor data without using 2D bounding boxes as an intermediate step, thereby reducing system complexity while maintaining the adaptability and versatility of 3D object understanding.
Data Source
AI summary
A method for inferring a location of an object includes extracting features from sensor data obtained from a number of sensors of an autonomous vehicle and encoding the features to a number of sensor space representations. The method also reshapes the number of sensor space representations to a feature space representation corresponding to a feature space of a spatial area. The method further identifies the object based on a mapping of the features to the feature space representation. The method still further projects a representation of the identified object to a location of the feature space and controls an action of the autonomous vehicle based on projecting the representation.