  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}.
