3D object detection for autonomous driving: A comprehensive survey
Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …
Deep learning for 3d point clouds: A survey
Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
CY Wang, A Bochkovskiy… - Proceedings of the IEEE …, 2023 - openaccess.thecvf.com
Real-time object detection is one of the most important research topics in computer vision.
As new approaches regarding architecture optimization and training optimization are …
As new approaches regarding architecture optimization and training optimization are …
Transfusion: Robust lidar-camera fusion for 3d object detection with transformers
LiDAR and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …
Segment anything model for medical images?
Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …
segmentation. It has achieved impressive results on various natural image segmentation …
Yolov9: Learning what you want to learn using programmable gradient information
Today's deep learning methods focus on how to design the most appropriate objective
functions so that the prediction results of the model can be closest to the ground truth …
functions so that the prediction results of the model can be closest to the ground truth …
Rtmdet: An empirical study of designing real-time object detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO
series and is easily extensible for many object recognition tasks such as instance …
series and is easily extensible for many object recognition tasks such as instance …
Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds
Y Zhang, Q Hu, G Xu, Y Ma, J Wan… - Proceedings of the …, 2022 - openaccess.thecvf.com
We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …
memory and computational cost, existing point-based pipelines usually adopt task-agnostic …
Virtual sparse convolution for multimodal 3d object detection
Abstract Recently, virtual/pseudo-point-based 3D object detection that seamlessly fuses
RGB images and LiDAR data by depth completion has gained great attention. However …
RGB images and LiDAR data by depth completion has gained great attention. However …
SE-SSD: Self-ensembling single-stage object detector from point cloud
Abstract We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate
and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both …
and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both …