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The formation of eyes led to the big bang of evolution. The dynamics changedfrom a primitive organism waiting for the food to come into contact for eatingfood being sought after by visual sensors. The human eye is one of the mostsophisticated developments of evolution, but it still has defects. Humans haveevolved a biological perception algorithm capable of driving cars, operatingmachinery, piloting aircraft, and navigating ships over millions of years.Automating these capabilities for computers is critical for variousapplications, including self-driving cars, augmented reality, and architecturalsurveying. Near-field visual perception in the context of self-driving cars canperceive the environment in a range of $0-10$ meters and 360{\deg} coveragearound the vehicle. It is a critical decision-making component in thedevelopment of safer automated driving. Recent advances in computer vision anddeep learning, in conjunction with high-quality sensors such as cameras andLiDARs, have fueled mature visual perception solutions. Until now, far-fieldperception has been the primary focus. Another significant issue is the limitedprocessing power available for developing real-time applications. Because ofthis bottleneck, there is frequently a trade-off between performance andrun-time efficiency. We concentrate on the following issues in order to addressthem: 1) Developing near-field perception algorithms with high performance andlow computational complexity for various visual perception tasks such asgeometric and semantic tasks using convolutional neural networks. 2) UsingMulti-Task Learning to overcome computational bottlenecks by sharing initialconvolutional layers between tasks and developing optimization strategies thatbalance tasks.

Surround-View Cameras based Holistic Visual Perception for Automated Driving