  The formation of eyes led to the big bang of evolution. The dynamics changed
from a primitive organism waiting for the food to come into contact for eating
food being sought after by visual sensors. The human eye is one of the most
sophisticated developments of evolution, but it still has defects. Humans have
evolved a biological perception algorithm capable of driving cars, operating
machinery, piloting aircraft, and navigating ships over millions of years.
Automating these capabilities for computers is critical for various
applications, including self-driving cars, augmented reality, and architectural
surveying. Near-field visual perception in the context of self-driving cars can
perceive the environment in a range of $0-10$ meters and 360{\deg} coverage
around the vehicle. It is a critical decision-making component in the
development of safer automated driving. Recent advances in computer vision and
deep learning, in conjunction with high-quality sensors such as cameras and
LiDARs, have fueled mature visual perception solutions. Until now, far-field
perception has been the primary focus. Another significant issue is the limited
processing power available for developing real-time applications. Because of
this bottleneck, there is frequently a trade-off between performance and
run-time efficiency. We concentrate on the following issues in order to address
them: 1) Developing near-field perception algorithms with high performance and
low computational complexity for various visual perception tasks such as
geometric and semantic tasks using convolutional neural networks. 2) Using
Multi-Task Learning to overcome computational bottlenecks by sharing initial
convolutional layers between tasks and developing optimization strategies that
balance tasks.
