Statistical optimization for geometric computation : theory and practice

By 金谷健一. 2005

357 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/bf8df4ac160436be6fad94d76e2200ac437bdeea

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Citations in the corpus

One-shot learning and generation of dexterous grasps for novel objects (2016, methodology): “As a result, we have The surface normal at p is computed from the nearest neighbours of p using a PCA-based method (e.g. (Kanatani, 2005)).”
Bayesian Model Estimation and Selection for Epipolar Geometry and Generic Manifold Fitting (2002, methodology): “Kanatani (1996) recognizes the problems with orthodox methods and proposes his own ‘geometric solution’.”
A Closed-Form Expression of the Positional Uncertainty for 3D Point Clouds (2009, unknown): “À ; ð24Þ where A À is the Moore-Penrose generalized inverse of matrix A [5], [19], [8].”
Robust localization using 3D NDT scan matching with experimentally determined uncertainty and road marker matching (2017, background): “where σ(2) denotes the scale of the covariance, and is called the noise level [23].”
Autonomous driving based on accurate localization using multilayer LiDAR and dead reckoning (2017, background): “where σ(2) is the noise level that adjusts the amount of noise [19] and ∂(2)J(x)/∂x(2) is the Hessian matrix of the cost function J .”
Quantitative Error Assessment in Pattern Matching: Effects of Intensity Pattern Noise, Interpolation, Strain and Image Contrast on Motion Measurements (2009, methodology): “Kanatani [3] presented a Cramer–Rao lower bound method and described its potential applicability in computer vision for error propagation.”
Visual SLAM and Structure from Motion in Dynamic Environments (2018, methodology): “Based on the limitation of previous works, Kanatani [71, 72] proposed Geometric Information Criterion (G-AIC, or in some literature called GIC) by taking into account the number of observations and the dimension of the model; it has the following form: where N is the number of data and D is the…”
Error analysis for circle fitting algorithms (2009, methodology): “Earlier studies, see e.g. [5, 8, 17], usually focused on the leading, i.e. second-order, terms only, disregarding all the fourth-order terms, and this is where our analysis is diﬀerent.”
Comparison of surface normal estimation methods for range sensing applications (2009, methodology): “Interestingly enough, this formulation is equivalent to the Maximum Likelihood estimation of the local plane described in [6] and [7].”