KAIST Image and Video Systems lab

Notice

Hit	375
Subject	[IEEE TIP] Stereoscopic Vision Recalling Memory for Monocular 3D Object Detection (Jung Uk Kim) is accepted in IEEE Transactions on Image Processing
Name	관리자
Date	2023-04-27
Title: Stereoscopic Vision Recalling Memory for Monocular 3D Object Detection Authors: Jung Uk Kim, Hyung-Il Kim, and Yong Man Ro Monocular 3D object detection has drawn increasing attention in various human-related applications, such as autonomous vehicles, due to its cost-effective property. On the other hand, a monocular image alone inherently contains insufficient information to infer the 3D information. In this paper, we propose a new monocular 3D object detector that can recall the stereoscopic visual information about an object, given a monocular each object by being aware of its location. Next, given the object appearance of the monocular image, we devise Monocular-to-tereoscopic (M2S) memory that can recall the object appearance of the counterpart view and corresponding depth information. For this purpose, we introduce a stereoscopic vision memorizing loss that guides M2S memory to store the stereoscopic visual information. Further, we propose a binocular vision association loss to guide M2S memory that can associate information of the left-right view about the object when estimating the depth. As a result, our monocular 3D object detector with M2S memory can effectively exploit the recalled stereoscopic visual information in the inference phase. The comprehensive experimental results on the two public datasets, KITTI 3D Object Detection Benchmark and Waymo Open Dataset, demonstrate the effectiveness of the proposed method. We claim that our method is a step forward method that follows the behaviors of humans that can recall the stereoscopic visual information even when one eye is closed. "Note: Jung Uk Kim is a professor at KyungHee University after completing his PhD."