Data Augmentation for Object Detection via Differentiable Neural Rendering (2103.02852v2)

Abstract: It is challenging to train a robust object detector under the supervised learning setting when the annotated data are scarce. Thus, previous approaches tackling this problem are in two categories: semi-supervised learning models that interpolate labeled data from unlabeled data, and self-supervised learning approaches that exploit signals within unlabeled data via pretext tasks. To seamlessly integrate and enhance existing supervised object detection methods, in this work, we focus on addressing the data scarcity problem from a fundamental viewpoint without changing the supervised learning paradigm. We propose a new offline data augmentation method for object detection, which semantically interpolates the training data with novel views. Specifically, our new system generates controllable views of training images based on differentiable neural rendering, together with corresponding bounding box annotations which involve no human intervention. Firstly, we extract and project pixel-aligned image features into point clouds while estimating depth maps. We then re-project them with a target camera pose and render a novel-view 2d image. Objects in the form of keypoints are marked in point clouds to recover annotations in new views. Our new method is fully compatible with online data augmentation methods, such as affine transform, image mixup, etc. Extensive experiments show that our method, as a cost-free tool to enrich images and labels, can significantly boost the performance of object detection systems with scarce training data. Code is available at \url{https://github.com/Guanghan/DANR}.

Data Augmentation for Object Detection via Differentiable Neural Rendering

The paper "Data Augmentation for Object Detection via Differentiable Neural Rendering" provides a novel approach to addressing the challenge of training robust object detectors when annotated data is scarce, a common problem in fields like robotics and autonomous vehicles. The authors propose an offline data augmentation method leveraging differentiable neural rendering to synthetically generate novel views of training images with accompanying bounding box annotations, reducing the need for expensive human labeling and enhancing data diversity without modifying the supervised learning paradigm.

The core contribution of this research lies in a system that generates novel 2D views of training images consistent with unspecified camera physiognomies, while maintaining semantic integrity and providing automatic bounding box annotations. Notably, the system's compatibility with existing online data augmentation methods, such as affine transformations and image mixup, implies that it can be integrated into standard pipelines to compound the benefits of various data augmentation techniques.

Key components of the proposed method include the extraction of pixel-aligned image features and their projection into point clouds with depth mapping. The point clouds are subsequently rendered into novel-view images via a differentiable neural rendering framework, which maintains the system's end-to-end differentiability. This design allows the generation of high-fidelity images with controllable diversity in the forms of camera positions and perspectives.

The authors present extensive evaluations demonstrating significant improvements in object detection performance across multiple datasets when using their augmentation method, particularly when training data is limited. Scalability is underscored by its applicability to different data scarcity levels and detector configurations, including both keypoint-based and proposal-based object detectors.

From a theoretical perspective, this approach challenges conventional data augmentation methods by situating the task within a 3D-aware framework that considers spatial semantics beyond mere pixel alterations. The practical implications are considerable: the technique suggests a pathway to improve model performance in data-scarce environments and potentially reduce biases introduced by traditional augmentation artifacts.

Looking forward, future work could explore the extension of differentiable neural renderer's training datasets to enhance generalizability across broader domains, such as outdoor imagery or varied lighting conditions, which would address current limitations when applied to comprehensive datasets like COCO. Additionally, improvements in training efficiency and scalability concerning computational resources remain crucial for broad adoption in large-scale applications.

In conclusion, this paper adds an innovative perspective to the problem of data scarcity in object detection, presenting a sophisticated technique that demonstrates notable performance gains and offers a promising tool for future advancements in data augmentation methodologies in artificial intelligence.