RT Conference Proceedings
T1 Mono-DCNet: Monocular 3D Object Detection via Depth-based Centroid Refinement and Pose Estimation
A1 Astudillo Olalla, Armando
A1 Al Kaff, Abdulla Hussein Abdulrahman
A1 García Fernández, Fernando
AB 3D object detection is a well-known problem for autonomous systems. Most of the existing methods use sensor fusion techniques with Radar, LiDAR, and Cameras. However, one of the challenges is to estimate the 3D shape and location of the adjoining vehicles from a single monocular image without other 3D sensors; such as Radar or LiDAR. To solve the lack of the depth information, a novel method for 3D vehicle detection is presented. In this work, instead of using the whole depth map and the viewing angle (allocentric angle), only the depth mask of each object is used to refine the projected centroid and estimate its egocentric angle directly. The performance of the proposed method is tested and validated using the KITTI dataset, obtaining similar results to other state-of-the-art methods for Monocular 3D Object Detection.
PB IEEE
SN 978-1-6654-8821-1
YR 2022
FD 2022-07-19
LK https://hdl.handle.net/10016/36723
UL https://hdl.handle.net/10016/36723
LA eng
NO Proceeding of 2022 IEEE Intelligent Vehicles Symposium (IV), (33rd IEEE IV), 04-09 June 2022, Aachen, Germany.
NO This work has been supported by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual Agreement with UC3M (“Fostering Young Doctors Research”, APBI-CMUC3M), and in the context of the V PRICIT (Research and Technological Innovation Regional Programme).Also, We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.
DS e-Archivo
RD 18 jul. 2024