Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes (1708.01566v1)

Abstract: The success of deep learning in computer vision is based on availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation and object detection models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment real images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance and a large number of complex object arrangements. In contrast to modeling complete 3D environments, our augmentation approach requires only a few user interactions in combination with 3D shapes of the target object. Through extensive experimentation, we conclude the right set of parameters to produce augmented data which can maximally enhance the performance of instance segmentation models. Further, we demonstrate the utility of our approach on training standard deep models for semantic instance segmentation and object detection of cars in outdoor driving scenes. We test the models trained on our augmented data on the KITTI 2015 dataset, which we have annotated with pixel-accurate ground truth, and on Cityscapes dataset. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amount of annotated real data.

Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes

The paper, "Augmented Reality Meets Computer Vision: Efficient Data Generation for Urban Driving Scenes," presents an innovative approach to data augmentation for training deep neural networks in the domain of semantic instance segmentation and object detection. The authors address a significant challenge in computer vision: the need for large annotated datasets necessary for the training of high-capacity models, such as deep neural networks. While synthetic data from 3D renderers offers an alternative, it often lacks the realism required for optimal performance.

The proposed approach bridges the gap between real and synthetic data by augmenting real-world imagery with virtual objects, specifically focusing on the urban driving scenario. Instead of creating complex 3D models of entire environments, the paper details a technique to overlay realistic virtual objects (such as cars) onto real-world backgrounds. This method leverages large-scale imagery, which is easily and inexpensively captured, while incorporating virtual elements to enhance and expand the training dataset's diversity.

Key findings highlighted by the paper demonstrate that models trained on augmented datasets outperform those trained solely on synthetic data or limited real data. This is supported by rigorous experimentation using datasets such as the KITTI 2015 and the Cityscapes dataset, where the augmented models showed superior generalization capabilities. The authors emphasize that the synergy between real backgrounds and synthetic objects provides significant advantages over purely synthetic training data and show that such augmented data leads to better generalization in deep learning models.

The augmentation pipeline described in the paper includes high-quality 3D models, environment maps, and realistic rendering techniques to ensure a seamless blend between virtual and real components. Various factors affecting the augmentation process, such as the number of synthetic objects, their placement, environment maps, and post-processing effects, are thoroughly analyzed, revealing insights into achieving a balance between realism and data diversity. Notably, the paper highlights the minimal manual effort required compared to building full virtual environments, marking an efficient strategy for data generation without compromising on dataset variability.

From a theoretical standpoint, this work underscores the importance of realism and data variety in mitigating model overfitting and enhancing performance in new environments. The empirical results indicate that with the right combination of real and synthetic components, it is possible to significantly extend the capabilities of current computer vision models. Practically, this research provides a cost-effective solution for generating extensive labeled datasets, key for advancing semantic segmentation and object detection in autonomous driving applications.

Looking forward, this methodology could inspire further developments in the field of augmented reality data generation, potentially impacting various application domains beyond autonomous driving. Continued exploration of generative adversarial networks or other advanced techniques might lead to even greater fidelity in data augmentation, enhancing model performance across diverse computer vision tasks. The implications of this efficient data generation method offer promising avenues for advancing the efficacy of AI systems in real-world settings.