目标检测是计算机视觉中一个著名的研究分支，近年来出现了许多最先进的目标检测算法，但这些目标检测器在密集目标检测中的性能如何？在本文中，我们回顾了常见的和高度准确的目标检测方法的场景中，许多相似的物体被放置在彼此非常接近。我们还表明，高斯图的多任务学习以及分类和限界框回归使我们的准确性比基线有了显着的提高。我们在现有的RetinaNet网络中引入了高斯层和高斯解码器，以便在密集场景中获得更好的准确性，计算成本与RetinaNet相同。我们显示，相对于基线RetinaNet，mAP的增益为6\%和5\%。我们的方法在SKU 110 K \cite{sku110k}数据集上也达到了最先进的精度。
英文摘要：
Object detection is a famous branch of research in computer vision, many state of the art object detection algorithms have been introduced in the recent past, but how good are those object detectors when it comes to dense object detection? In this paper we review common and highly accurate object detection methods on the scenes where numerous similar looking objects are placed in close proximity with each other. We also show that, multi-task learning of gaussian maps along with classification and bounding box regression gives us a significant boost in accuracy over the baseline. We introduce Gaussian Layer and Gaussian Decoder in the existing RetinaNet network for better accuracy in dense scenes, with the same computational cost as the RetinaNet. We show the gain of 6\% and 5\% in mAP with respect to baseline RetinaNet. Our method also achieves the state of the art accuracy on the SKU110K \cite{sku110k} dataset.
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
Fudan University（复旦大学）；School of Future Technology, Harbin Institute of Technology（哈尔滨工业大学未来技术学院）；Department of Computer Science and Engineering, Shanghai Jiao Tong University（上海交通大学计算机科学与工程系）
RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid Gaussian Splatting
Institute of Robotics and Automatic Information System, Nankai University（南开大学机器人与自动信息系统研究所）；Tianjin Key Laboratory of Intelligent Robotics（天津市智能机器人重点实验室）；TBI center, Nankai University（南开大学TBI中心）
Efficient Sparse-to-Dense Visual Localization via Compact Gaussian Scene Representation and Accelerated Dense Pose Estimation
Electronic Information School, Wuhan University（武汉大学电子信息学院）；School of Robotics, Wuhan University（武汉大学机器人学院）；Electronic Information School and the School of Robotics, Wuhan University（武汉大学电子信息学院和机器人学院）
OG-Mapping: Octree-based Structured 3D Gaussians for Online Dense Mapping
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University（北京航空航天大学虚拟现实技术与系统国家重点实验室）；PengCheng Laboratory（鹏城实验室）；School of Computer Science and Technology, Shandong University（山东大学计算机科学与技术学院）；Beijing Technology and Business University（北京工商大学）
SemGauss-SLAM: Dense Semantic Gaussian Splatting SLAM
School of Automation and Intelligent Sensing, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai Jiao Tong University, Shanghai（自动化与智能感知学院、系统控制与信息处理重点实验室、中华人民共和国教育部、上海交通大学、上海）；University of Cambridge, United Kingdom（剑桥大学、英国）
FGS-SLAM: Fourier-based Gaussian Splatting for Real-time SLAM with Sparse and Dense Map Fusion
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences（中国科学院沈阳自动化研究所机器人重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Mechanical and Energy Engineering, Southern University of Science and Technology（南方科技大学机械与能源工程系）
Improving Dense Crowd Counting Convolutional Neural Networks using Inverse k-Nearest Neighbor Maps and Multiscale Upsampling
The Graduate Center of the City University of New York（纽约城市大学研究生中心）；Borough of Manhattan Community College - CUNY（纽约城市大学曼哈顿社区学院）；The City College of New York - CUNY（纽约城市大学纽约城市学院）
Predicting Dense and Context-aware Cost Maps for Semantic Robot Navigation
University of Bonn（波恩大学）；Robert Bosch GmbH（罗伯特·博世有限公司）；University of Oxford（牛津大学）；Lamarr Institute for Machine Learning and Artificial Intelligence（拉马尔机器学习与人工智能研究所）
OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding
School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；National Key Lab of Autonomous Intelligent Unmanned Systems, Beijing Institute of Technology（北京理工大学自主智能无人系统全国重点实验室）
Compact 3D Gaussian Splatting For Dense Visual SLAM
Shanghai Jiao Tong University（上海交通大学）；Nanyang Technological University（南洋理工大学）；The University of Tokyo（东京大学）；Harvard University（哈佛大学）；University of Cambridge（剑桥大学）
