3d object detection from images for autonomous driving: a survey
3D object detection from images, one of the fundamental and challenging problems in
autonomous driving, has received increasing attention from both industry and academia in …
autonomous driving, has received increasing attention from both industry and academia in …
A comprehensive review on 3D object detection and 6D pose estimation with deep learning
Nowadays, computer vision with 3D (dimension) object detection and 6D (degree of
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …
freedom) pose assumptions are widely discussed and studied in the field. In the 3D object …
Cross-view transformers for real-time map-view semantic segmentation
B Zhou, P Krähenbühl - … of the IEEE/CVF conference on …, 2022 - openaccess.thecvf.com
We present cross-view transformers, an efficient attention-based model for map-view
semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping …
semantic segmentation from multiple cameras. Our architecture implicitly learns a mapping …
Is pseudo-lidar needed for monocular 3d object detection?
Recent progress in 3D object detection from single images leverages monocular depth
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …
estimation as a way to produce 3D pointclouds, turning cameras into pseudo-lidar sensors …
Vision-centric bev perception: A survey
Object detection, a fundamental and challenging problem in computer vision, has
experienced rapid development due to the effectiveness of deep learning. The current …
experienced rapid development due to the effectiveness of deep learning. The current …
Cobevt: Cooperative bird's eye view semantic segmentation with sparse transformers
Bird's eye view (BEV) semantic segmentation plays a crucial role in spatial sensing for
autonomous driving. Although recent literature has made significant progress on BEV map …
autonomous driving. Although recent literature has made significant progress on BEV map …
[HTML][HTML] Radar sensor based machine learning approach for precise vehicle position estimation
Estimating vehicles' position precisely is essential in Vehicular Adhoc Networks (VANETs)
for their safe, autonomous, and reliable operation. The conventional approaches used for …
for their safe, autonomous, and reliable operation. The conventional approaches used for …
Grif net: Gated region of interest fusion network for robust 3d object detection from radar point cloud and monocular image
Robust and accurate scene representation is essential for advanced driver assistance
systems (ADAS) such as automated driving. The radar and camera are two widely used …
systems (ADAS) such as automated driving. The radar and camera are two widely used …
[HTML][HTML] A survey on deep learning based methods and datasets for monocular 3D object detection
Owing to recent advancements in deep learning methods and relevant databases, it is
becoming increasingly easier to recognize 3D objects using only RGB images from single …
becoming increasingly easier to recognize 3D objects using only RGB images from single …
Monocinis: Camera independent monocular 3d object detection using instance segmentation
J Heylen, M De Wolf, B Dawagne… - Proceedings of the …, 2021 - openaccess.thecvf.com
Monocular 3D object detection has recently shown promising results, however there remain
challenging problems. One of those is the lack of invariance to different camera intrinsic …
challenging problems. One of those is the lack of invariance to different camera intrinsic …