SoftPOSIT: Simultaneous Pose and Correspondence Determination

By Philip David, Daniel DeMenthon, Ramani Duraiswami, Hanan Samet. International Journal of Computer Vision, 2002

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Reconstruction of Personalized 3D Face Rigs from Monocular Video (2016, methodology): “The rigid head pose (R and t) is initialized using the POSIT algorithm [David et al. 2004] on the detected facial landmarks, and (α, δ) are initialized by solving Equation (10) with the parametric priors Eprob1(α), Esparse(δ), and Ebound (δ), that is, we optimize for (α, δ) using only the facial fea”
Vision-Based Pose Estimation From Points With Unknown Correspondences (2014, methodology): “So far, the most effective method to solve this problem is the SoftPOSIT method [7].”
Linear regression with sparsely permuted data (2017, background): “The papers [3, 42] provide extensive lists including multi-target tracking in radar systems [45] and pose and correspondence estimation in computer vision [20].”
Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views (2015, methodology): “3D Object Detection Most 3D object detection methods are based on representing objects with discriminative features for points [4], patches [6] and parts [19, 29, 34], or by exploring topological structures [15, 2, 3].”
The ApolloScape Open Dataset for Autonomous Driving and Its Application (2018, background): “Usually in a large environment, a pose prior is required in order to obtain good estimation [43], [44].”