3D Construction Network Training With Radar-Supervised Depth
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Solution Overview
Problem
Existing three-dimensional scene construction methods face inaccuracies in predicting color values and depths, particularly in scenarios like autonomous driving and augmented reality, due to insufficient geometric consistency and depth prediction in neural radiance field networks.
Innovation Solution
A three-dimensional construction network training method that incorporates radar point cloud data as deep supervision, using iterative training with virtual camera views and geometric consistency constraints to enhance depth accuracy, employing a neural radiance field (NeRF) network trained on multiple frames of images and radar data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a trained model is used to output three-dimensional scene models, then the model can be generated efficiently, but the prediction accuracy of color values and depths is insufficient
Solution Approach 1:
The patent combines multiple data sources (radar point cloud data and image data) and multiple loss functions (depth loss and color loss) into a unified training framework. This merging allows the model to simultaneously optimize for both depth accuracy and color accuracy, resolving the contradiction between efficient model generation and precise depth prediction.
Solution Approach 2:
The patent implements a feedback mechanism through deep supervision using radar point cloud data during the training process. The radar depth information provides ground truth feedback that guides the optimization of depth predictions, enabling the model to learn accurate depth values while maintaining efficient generation capabilities.
2Measurement precision
If radar point cloud data is used as deep supervision, then depth accuracy is improved, but the system complexity increases
Solution Approach 1:
The trained three-dimensional construction network serves multiple functions: it predicts both depth values and color values from input images. By making the model multi-functional, the patent reduces the need for separate specialized systems, thereby managing complexity while improving depth accuracy through radar supervision.
Solution Approach 2:
The patent uses the three-dimensional construction network as an intermediary that processes and integrates information from multiple sources (images and radar data). This intermediary model consolidates the complexity of fusing multiple data types into a unified prediction system, making the overall system more manageable while achieving high depth accuracy.
3Stability of the object's composition
If multiple frames of images and radar data are used for joint training, then geometric consistency is improved, but the training complexity and data processing requirements increase
Solution Approach 1:
The patent segments the training process into distinct components: image data processing, radar data processing, and integrated joint training. By segmenting the complex training task into manageable parts with separate pipelines, the system can handle multiple frames of images and radar data while controlling training complexity through modular architecture.
Solution Approach 2:
The patent creates a composite training framework that integrates heterogeneous data types (image data and radar point cloud data) into a unified training process. This composite approach allows the model to learn geometric consistency from multiple sources simultaneously, improving structural stability while managing complexity through a unified loss function and training pipeline.
Data Source
AI summary
This application provides a three-dimensional construction network training method and apparatus, and a three-dimensional model generation method and apparatus in the field of computer vision, to perform joint training based on a plurality of frames of images and radar point cloud data to obtain a three-dimensional construction network. More accurate depths included in the radar point cloud data may be used as deep supervision. The method includes: obtaining the plurality of frames of images and a photographing parameter used by a camera device when the plurality of frames of images are photographed, where the plurality of frames of images include images photographed from a plurality of views; obtaining the radar point cloud data including photographing scenario data; and obtaining the three-dimensional construction network based on the plurality of frames of images, the photographing parameter, and the radar point cloud data.