Single-View 3D Object Detection With Unified Neural Reconstruction
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Solution Overview
Problem
Existing single view 3D object reconstruction techniques are limited by sparse data sources and require multi-view observations and accurate object poses, leading to computational overhead and inefficiencies.
Innovation Solution
A unified object-centric neural reconstruction (UPNeRF) approach that integrates object-centric neural reconstruction and pose estimation to generate refined poses from input images, reducing reliance on external 3D object detection and enhancing computational efficiency and generalizability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multi-view observations and accurate object poses are used for 3D reconstruction, then reconstruction accuracy is improved, but computational overhead and system complexity increase
Solution Approach 1:
The patent combines pose estimation and 3D reconstruction into a unified single-view framework. The neural network simultaneously performs pose estimation and reconstructs 3D geometry from a single image, eliminating the need for separate multi-view capture systems and reducing overall system complexity while maintaining reconstruction accuracy
Solution Approach 2:
The patent transitions from requiring multiple 2D views to achieving 3D reconstruction from a single 2D image by introducing a neural network-based implicit representation. This dimensional transformation allows the system to infer depth and 3D structure from limited 2D input without needing multiple observation angles
2Measurement precision
If multi-view observations are used for 3D reconstruction, then reconstruction accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent pre-trains the neural network on large datasets of images and 3D models before deployment. This preliminary training enables the network to perform pose estimation and 3D reconstruction rapidly during inference, avoiding the need for computationally intensive multi-view processing at runtime while maintaining high accuracy
3Measurement precision
If accurate object poses are required as input, then reconstruction quality is improved, but the system becomes more dependent on external detection systems
Solution Approach 1:
The patent merges pose estimation and 3D reconstruction into a single integrated neural network that processes a single input image to produce both pose estimates and 3D reconstruction outputs. This unified approach eliminates dependency on external pose detection systems while maintaining reconstruction quality through joint optimization of both tasks
Data Source
AI summary
A method of performing pose estimation for images includes, at one or more processing devices, receiving an input image, generating, based on the input image, a pose code that corresponds to an estimate pose of an object in the input image, generating a box code corresponding to a bounding box of the object in the input image, performing pose estimation for the input image by generating a refined pose of the object using the pose code and the box code, generating a prediction output for the object in the input image based on the input image and the refined pose, and controlling one or more functions of a device based on the prediction output.