Neural Radiance Fields for Automated Object Detector Data Generation
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Solution Overview
Problem
Collecting large amounts of labelled training data for object detection in autonomous systems is time-consuming, labor-intensive, and costly, particularly in applications like autonomous driving and industrial automation.
Innovation Solution
A method using neural radiance fields (NeRF) to generate efficient training data by training two neural radiance fields: one for feature embedding and another for multimodal sensor data, allowing auto-labelling without manual intervention, leveraging language embeddings like CLIP features for flexible and robust 3D labelling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labelling or pre-trained object detectors are used to generate training data, then ground truth information can be obtained, but the process becomes time-consuming, labor-intensive and costly
Solution Approach 1:
The patent uses neural radiance fields to create a virtual copy of the real scene, allowing training data to be generated from synthetic renders rather than manual labelling of real sensor data. The NeRF learns to represent the scene from multiple views and can generate infinite training examples through rendering, eliminating the need for time-consuming manual annotation while maintaining ground truth quality.
Solution Approach 2:
The system performs self-service by automatically generating its own training data through the NeRF model. Once the NeRF is trained on a small set of multi-view images, it can autonomously generate labelled training data for various sensor modalities without requiring external manual intervention or pre-trained object detectors, making the process efficient and scalable.
2Reliability
If large amounts of labelled training data are collected for object detection, then detection accuracy improves, but the data collection process becomes labor-intensive and costly
Solution Approach 1:
Instead of collecting and manually labelling large amounts of real sensor data, the patent creates synthetic copies of the scene through NeRF rendering. This allows unlimited generation of training data with automatic ground truth labels, improving detection accuracy without the labor-intensive data collection process.
Solution Approach 2:
The NeRF model is trained in advance on a small set of multi-view images to learn the scene representation. This preliminary action enables the subsequent automatic generation of unlimited labelled training data for object detection, making the overall process easier and more efficient.
3Reliability
If multiple sensor modalities are used for object detection, then detection robustness improves, but the complexity of sensor setup and data processing increases
Solution Approach 1:
The patent uses a single NeRF model to represent multiple sensor modalities simultaneously. By training the NeRF on multi-view images and using it to render synthetic data for different sensor types, the system achieves multi-modal detection robustness without the complexity of actual multi-sensor hardware setup and synchronization.
Solution Approach 2:
The NeRF model serves as a universal representation that can generate data for multiple sensor modalities (RGB images, depth maps, normal maps, etc.) from a single scene understanding. This multi-functionality allows the system to achieve robust object detection across different sensor types without increasing hardware complexity.
Data Source
AI summary
A method for generating training data for an object detector. The method includes receiving a plurality of optical images of a scene, each camera showing the scene from a respective viewing direction of a plurality of different viewing directions, receiving a plurality of sensor data elements, each sensor data element including sensor data other than optical image data of the scene from a respective sensing direction of a plurality of different sensing directions, training a first neural radiance field using the plurality of optical images to generate, for each 3D point of the scene, a respective value of a predetermined feature, training a second neural radiance field using the plurality of sensor data elements to generate, for each 3D point of the scene, a respective sensor data value and generating training data elements for the object detector using the first and the second neural radiance field.