TY - INPR A1 - Pederiva, Marcelo Eduardo A1 - De Martino, José Mario A1 - Zimmer, Alessandro T1 - MonoNext: A 3D Monocular Object Detection with ConvNext N2 - Autonomous driving perception tasks rely heavily on cameras as the primary sensor for Object Detection, Semantic Segmentation, Instance Segmentation, and Object Tracking. However, RGB images captured by cameras lack depth information, which poses a significant challenge in 3D detection tasks. To supplement this missing data, mapping sensors such as LIDAR and RADAR are used for accurate 3D Object Detection. Despite their significant accuracy, the multi-sensor models are expensive and require a high computational demand. In contrast, Monocular 3D Object Detection models are becoming increasingly popular, offering a faster, cheaper, and easier-to-implement solution for 3D detections. This paper introduces a different Multi-Tasking Learning approach called MonoNext that utilizes a spatial grid to map objects in the scene. MonoNext employs a straightforward approach based on the ConvNext network and requires only 3D bounding box annotated data. In our experiments with the KITTI dataset, MonoNext achieved high precision and competitive performance comparable with state-of-the-art approaches. Furthermore, by adding more training data, MonoNext surpassed itself and achieved higher accuracies. UR - https://doi.org/10.48550/arXiv.2308.00596 Y1 - 2023 UR - https://doi.org/10.48550/arXiv.2308.00596 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38846 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Pederiva, Marcelo Eduardo A1 - De Martino, José Mario A1 - Zimmer, Alessandro ED - Osten, Wolfgang T1 - A light perspective for 3D object detection T2 - Seventeenth International Conference on Machine Vision (ICMV 2024) UR - https://doi.org/10.1117/12.3055035 Y1 - 2025 UR - https://doi.org/10.1117/12.3055035 SN - 978-1-5106-8827-8 SN - 978-1-5106-8828-5 PB - SPIE CY - Bellingham ER - TY - CHAP A1 - Vriesman, Daniel A1 - Pederiva, Marcelo Eduardo A1 - De Martino, José Mario A1 - Britto Junior, Alceu A1 - Zimmer, Alessandro A1 - Brandmeier, Thomas T1 - A fusion approach for pre-crash scenarios based on lidar and camera sensors T2 - 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), Proceedings UR - https://doi.org/10.1109/VTC2021-Spring51267.2021.9449039 KW - sensor fusion KW - lidar KW - camera KW - pre-crash KW - ADAS Y1 - 2021 UR - https://doi.org/10.1109/VTC2021-Spring51267.2021.9449039 SN - 978-1-7281-8964-2 PB - IEEE CY - Piscataway ER -