In this paper, we present a visual learning framework to retrieve a 3D model and estimate its pose from a single image. To increase the quantity and quality of training data, we define our simulation space in the near infrared (NIR) band, and utilize the quasi-Monte Carlo (MC) method for scalable photorealistic rendering of manufactured components. Two types of convolutional neural network (CNN) architectures are trained over these synthetic data and a relatively small amount of real data. The first CNN model seeks the most discriminative information and uses it to classify industrial components with fine-grained shape attributes. Once a 3D model is identified, one of the category-specific CNNs is tested for pose regression in the second phase. The mixed data for learning object categories is useful in domain adaptation and attention mechanism in our system. We validate our data-driven method with 88 component models, and the experimental results are qualitatively demonstrated. Also, the CNNs trained with various conditions of mixed data are quantitatively analyzed.

Recently, there have been significant advances in self-driving cars, which will play key roles in future intelligent transportation systems. In order for these cars to be successfully deployed on real roads, they must be able to autonomously drive along collision-free paths while obeying traffic laws. In contrast to many existing approaches that use prebuilt maps of roads and traffic signals, we propose algorithms and systems using Unified Map built with various onboard sensors to detect obstacles, other cars, traffic signs, and pedestrians. The proposed map contains not only the information on real obstacles nearby but also traffic signs and pedestrians as virtual obstacles. Using this map, the path planner can efficiently find paths free from collisions while obeying traffic laws. The proposed algorithms were implemented on a commercial vehicle and successfully validated in various environments, including the 2012 Hyundai Autonomous Ground Vehicle Competition.

A typical gaze estimator needs an explicit personal calibration stage with many discrete fixation points. This limitation can be resolved by mapping multiple eye images and corresponding saliency maps of a video clip during an implicit calibration stage. Compared to previous calibration-free methods, our approach clusters eye images by using Gaussian Mixture Model (GMM) in order to increase calibration accuracy and reduce training redundancy. Eye feature vectors representing eye images undergo soft clustering with GMM as well as the corresponding saliency maps for aggregation. The GMM based soft-clustering boosts the accuracy of Gaussian process regression which maps between eye feature vectors and gaze directions given this constructed data. The experimental results show an increase in gaze estimation accuracy compared to previous works on calibration-free method.