Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications (1708.05869v2)

Abstract: We present a photo-realistic training and evaluation simulator (Sim4CV) with extensive applications across various fields of computer vision. Built on top of the Unreal Engine, the simulator integrates full featured physics based cars, unmanned aerial vehicles (UAVs), and animated human actors in diverse urban and suburban 3D environments. We demonstrate the versatility of the simulator with two case studies: autonomous UAV-based tracking of moving objects and autonomous driving using supervised learning. The simulator fully integrates both several state-of-the-art tracking algorithms with a benchmark evaluation tool and a deep neural network (DNN) architecture for training vehicles to drive autonomously. It generates synthetic photo-realistic datasets with automatic ground truth annotations to easily extend existing real-world datasets and provides extensive synthetic data variety through its ability to reconfigure synthetic worlds on the fly using an automatic world generation tool. The supplementary video can be viewed a https://youtu.be/SqAxzsQ7qUU

• The paper introduces Sim4CV, a photo-realistic simulator built on Unreal Engine 4 for comprehensive CV algorithm training.
• It provides automated ground-truth annotations, real-time benchmarking, and diverse environments for robust testing of tracking and navigation methods.
• Numerical evaluations show that simulator-generated data improves deep neural network performance in autonomous driving and UAV tracking tasks.

An Overview of Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications

The paper "Sim4CV: A Photo-Realistic Simulator for Computer Vision Applications" presents a comprehensive simulator, Sim4CV, designed to facilitate training and evaluation of computer vision (CV) algorithms. Built on the Unreal Engine 4, Sim4CV offers realistic simulations of various environments, rendering itself highly applicable across numerous CV scenarios such as autonomous driving, UAV tracking, object detection, and more. The simulator integrates sophisticated physics for vehicles, UAVs, and dynamic human actors, promoting its use in diverse urban and suburban landscapes.

Simulator Capabilities and Applications

Sim4CV focuses on providing a rich, configurable environment that allows researchers to generate varied synthetic datasets with automated ground-truth annotations. The simulator is equipped with tools for real-time benchmarking and integrates state-of-the-art tracking algorithms. It has been showcased through two main applications: autonomous UAV-based tracking and supervised learning in autonomous driving.

The first application explores real-time UAV tracking, where the simulator evaluates the performance of various tracking algorithms by simulating UAV tracking of moving cars. This feature emphasizes the simulator's utility in testing and refining object tracking methods in controlled conditions that mimic real-world complexities.

The second application focuses on autonomous driving and uses deep neural networks (DNNs) to predict waypoints for vehicle navigation. The approach is modular, allowing distinct paths for determining vehicle controls and environmental interaction, thereby showcasing flexibility in application without relying on extensive manually collected data.

Numerical Results and Evaluation

Numerical evaluation reveals that Sim4CV can serve as an effective platform for comparing the precision and success of tracking algorithms. The research demonstrated that, of the five trackers evaluated, MEEM exhibited superior performance in both metrics over sequences containing a range of driving speeds and environments. Furthermore, the integration of DNNs with Sim4CV for autonomous driving tasks demonstrated that models trained using simulator-generated data can outperform human drivers regarding accuracy.

Implications and Future Directions

The development of Sim4CV holds significant implications for both theoretical and practical advancements in AI and CV. The capacity to simulate environments with high fidelity provides an essential tool for algorithm evaluation and training, possibly reducing the need for costly real-world data collection. The simulator's adaptability in generating diverse environments can facilitate robust training of models, potentially enhancing their generalization abilities.

Looking to the future, researchers might explore reinforcement learning approaches within Sim4CV, leveraging its capability to simulate real-world dynamics. Moreover, continual enhancements in scene diversity and realism may bridge any existing gaps between simulated data and real-world application, further enabling effective transfer learning methodologies. By integrating such advancements, Sim4CV could become indispensable for developing autonomous systems poised for real-world deployment.

In conclusion, Sim4CV represents a versatile and open-source contribution to the CV community, supporting a broad spectrum of applications and encouraging innovative research directions, particularly in autonomous navigation and UAV technologies.