详细解读可参见这篇文章:agent:[CVPRW 2020] SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation 论文阅读 论文动机 之前的两阶段单目3D目标检测框架都是先基于2D目标检测网络生成2D候选区域,然后在对候选区域去预测目标的3D属性,论文认为2D检测是冗余的,应该直接回归3D属性,因此论文就提出了一...
Monocular 3D object detection is challenging due to the lack of accurate depth information. Some methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB images. Depth-base
Recent advancements in Transformer-based monocular 3D object detection techniques have exhibited exceptional performance in inferring 3D attributes from si
The success of monocular 3D object detection highly relies on considerable labeled data, which is costly to obtain. To alleviate the annotation effort, we propose MVC-MonoDet, the first semi-supervised training framework that improves Monocular 3D object
This repository is the official implementation of our paperSMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. For more details, please see our paper. Introduction SMOKE is areal-timemonocular 3D object detector for autonomous driving. The runtime on a single NVIDIA TITAN XP...
这篇论文提出了一种end-to-end、single-stage的单目3D目标检测网络DD3D(Dense Depth-pre-trained 3D Detector),通过设计在深度估计和3D检测之间进行有效的信息传输,使得能够在大量的未标记预训练数据量上进行扩展学习。最终达到既可以像伪激光雷达方法一样从基于大量数据的深度估计预训练中受益,同时又拥有端到端方法...
M3dssd: Monocular 3d single stage object detector. In CVPR, 2021. 2 [32] Xinzhu Ma, Yinmin Zhang, Dan Xu, Dongzhan Zhou, Shuai Yi, Haojie Li, and Wanli Ouyang. Delving into localization errors for monocular 3d object detection. In CVPR, 2021. 2, 3 [33] Denn...
However, it is non-trivial to make a general adapted 2D detector work in this 3D task. In this technical report, we study this problem with a practice built on fully convolutional single-stage detector and propose a general framework FCOS3D. Specifically, we first transform the commonly ...
In the 3D object detection domain, PPT [36] investigates the utilization of extensive 3D point cloud data from diverse datasets for pre-training detectors. In addition, Uni3DETR [35] reveals how to de- vise a unified point-based 3D object detector that behaves well in different domains. For...
a one-stage 3D vehicle localization network CenterLoc3D is proposed, which contains three modules: backbone, multi-scale feature fusion, and multi-task detection head. In multi-scale feature fusion, we propose a weighted-fusion module to fuse five feature maps containing multi-scale information wit...