Computer Vision Lab

Due to the recent development of the VR and AR content industry , the demand for 3D content is increasing. However 3D content demands a lot of cost so the supply is not up to the demand. Although research to automate generating 3D contents is one of the oldest topics in the field of computer vision, classical methods have accumulated errors during passing huge pipelines. Due to the development of neural networks, representing 3D scene as neural networks which can represent 3D scene implicit and continuous is available. Therefore, in recent years, research is actively underway to increase the quantitative and qualitative performance of 3D reconstruction through such neural presentation.

Object detection in 3D space plays a crucial role in various real-world applications, including autonomous driving systems. Existing 3D object detection methods based on point clouds from LiDAR sensors often yield reliable results, but these methods suffer from a large budget to establish LiDAR sensors per vehicle. Further, camera-based object detection methods using monocular images are economical but, their performance is suboptimal due to insufficient depth cues. Recently, multi-view (and surround-view) camera systems have become an alternative balanced option as they can resolve some of the weaknesses of monocular and stereo vision systems for the 3D object detection task, potentially replacing LiDAR sensors.

Video contains not only visual information but also sound information. This multi-modal information helps understanding content, and it is more descriptive than visual information. Fusing this multi-modal information is an open question, and it is the first step toward general artificial intelligence. Additionally, there are a lot of different kinds of visual sensors which provide complementary information. Fusing this heterogeneous information reduces the uncertainty of the estimation model. In our lab, we research the integration of multiple sensor data with deep learning models.

Large-scale dataset curation and developing efficient annotation methods are crucial for deep learning since deep learning requires a large-scale dataset to optimize the deep neural networks. For this reason, the performance of the model has a positive correlation with the size and quality of the dataset.

Machine perception has been widely researched in the computer vision community due to its importance in many real-life applications. A recent breakthrough in machine perception by introducing deep learning significantly increased the usage of visual machine perception. In particular, hand/body pose estimation, object detection, object recognition, and pixel-wise segmentation have been opened the door to many real-life applications.

In computer vision, view synthesis has been used to apply changes in lighting and viewpoint to single-view images of rigid and non-rigid objects. In real-life applications, synthetic views can be used to help predict unobserved part locations and also improve the performance of object grasping with manipulators and the path planning of an autonomous driving system.