Differential Neural Rendering for Object Detection Data Augmentation
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Solution Overview
Problem
Existing object detection neural networks face challenges in training when annotated data is scarce, as traditional data augmentation methods fail to provide diverse and natural-looking data, leading to overfitting and the need for additional labeling.
Innovation Solution
A system that generates augmented data using differential neural rendering, which projects a 2D image with a bounding box into a 3D point cloud and projects it back into novel camera poses, creating new images with automatically labeled bounding boxes, thereby enhancing the diversity of training data without requiring additional pixel-level annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional online data augmentation methods (random crop, image mix-up) are used, then additional data diversity is provided to avoid overfitting, but unseen semantics and novel locations are not provided
Solution Approach 1:
The patent transforms 2D images into 3D point clouds with depth information, then renders them back to 2D images from novel camera poses. This dimensional transformation enables the generation of unseen semantics and locations by viewing objects from different angles, resolving the limitation of traditional augmentation methods that operate only in 2D space.
Solution Approach 2:
The patent creates synthetic copies of training images by rendering 3D point clouds from novel camera poses. These copied images provide diverse viewpoints and semantics while preserving the original object structures, enabling the model to learn from multiple perspectives without requiring additional real annotated data.
2Quantity of substance
If offline data augmentation synthesizes training images by alpha compositing, then additional data is provided, but the synthesized images are not natural and introduce artifacts that may impair the detector model
Solution Approach 1:
The patent replaces the mechanical alpha compositing process with a neural rendering pipeline that transforms 2D images to 3D point clouds and back to 2D images. This substitution eliminates the artifacts introduced by simple compositing operations, producing more natural-looking synthesized images that maintain visual fidelity.
3Quantity of substance
If offline data augmentation requires additional pixel-level labeling, then more annotated data is available, but the labeling process is time-consuming and complex
Solution Approach 1:
The patent enables the synthesis pipeline to automatically generate bounding boxes and annotations for rendered images by transforming the original annotations through the 3D point cloud representation. This self-service capability eliminates the need for manual pixel-level labeling of synthesized images, significantly reducing annotation time while maintaining data quality.
4Reliability
If more annotated data is collected for training, then object detection performance improves, but the cost and time for data collection and annotation increase
Solution Approach 1:
The patent creates synthetic copies of annotated training images by rendering 3D point clouds from novel camera poses. These synthesized images automatically inherit annotations through the 3D transformation process, providing additional training data without the time and cost of collecting and annotating new real-world images.
Data Source
AI summary
A system and a method for object detection using augmented training dataset. The system includes a computing device, which is configured to: provide a two-dimensional (2D) image, extract feature vectors and estimate depths of 2D image pixels, generate a point cloud using the feature vectors and the depths, and project the point cloud using a new camera pose to obtain a projected image. The 2D image has a bounding box enclosing an object and labeled with the object. Each pixel within the bounding box is named bounding box pixel, each point in the point cloud corresponding to the bounding box pixel is named bounding box point, each image pixel in the projected image corresponding to the bounding box point is named projected bounding box pixel, and a projected bounding box is defined using the projected bounding box pixels and labeled with the object.