Plane Estimation(平面估计)研究综述
Plane Estimation 平面估计 - The method allows us to detect planar patches by filtering and clustering so-called superpoints, whereby the well-known but suitably modified random sampling and consensus (RANSAC) approach plays a key role for plane estimation in outlier-rich data. [1] The plane modeling is a prediction method based on a plane estimation from depth pixels in a block. [2] In this context, the objective of this paper is to propose a hybrid point cloud compression solution based on plane estimation and coding that enhances the octree structure, thus exploiting two representation models for point clouds. [3]该方法允许我们通过过滤和聚类所谓的超点来检测平面块，其中众所周知但经过适当修改的随机采样和一致性 (RANSAC) 方法在异常值丰富的数据中对平面估计起着关键作用。 [1] 平面建模是一种基于块中深度像素的平面估计的预测方法。 [2] 在这种情况下，本文的目的是提出一种基于平面估计和编码的混合点云压缩解决方案，该解决方案增强了八叉树结构，从而利用了两种点云表示模型。 [3]
Ground Plane Estimation 地平面估计
The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. [1] Constructing Birds-Eye-View (BEV) maps from monocular images is typically a complex multi-stage process involving the separate vision tasks of ground plane estimation, road segmentation and 3D object detection. [2] An adaptive ground plane estimation method is exploited under the monocular camera for 3D geometric back-projection. [3] In this paper, we propose a novel and robust method for 3D localization of monocular visual objects in road scenes by joint integration of depth estimation, ground plane estimation, and multi-object tracking techniques. [4] An obstacle detection method based on multiframe point cloud fusion and ground plane estimation is proposed. [5]然而，驾驶环境的不可预测性和相机校准产生的噪声使传统的地平面估计不可靠，并对跟踪结果产生不利影响。 [1] 从单目图像构建鸟瞰图 (BEV) 地图通常是一个复杂的多阶段过程，涉及地平面估计、道路分割和 3D 对象检测等单独的视觉任务。 [2] 在单目相机下采用自适应地平面估计方法进行 3D 几何反投影。 [3] 在本文中，我们通过深度估计、地平面估计和多目标跟踪技术的联合集成，提出了一种新颖且稳健的道路场景中单目视觉对象的 3D 定位方法。 [4] 提出了一种基于多帧点云融合和地平面估计的障碍物检测方法。 [5]
Symmetry Plane Estimation
The CBM performance for projectile spectator symmetry plane estimation is studied with GEANT4 Monte Carlo simulations using collisions of gold ions with beam momentum of 12A GeV/c generated with the DCM-QGSM-SMM model. [1] We inspect the symmetry plane estimation on a real scan of an anthropomorphic human head phantom and show the robustness using a synthetic dataset. [2]使用 GEANT4 蒙特卡罗模拟研究了弹丸观察者对称平面估计的 CBM 性能，该模拟使用金离子与 DCM-QGSM-SMM 模型生成的 12A GeV/c 束动量的碰撞。 [1] 我们在拟人化人体头部模型的真实扫描上检查对称平面估计，并使用合成数据集显示鲁棒性。 [2]