81 lines
2.9 KiB
C
81 lines
2.9 KiB
C
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// ----------------------------------------------------------------------------
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// - Open3D: www.open3d.org -
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// ----------------------------------------------------------------------------
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// Copyright (c) 2018-2023 www.open3d.org
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// SPDX-License-Identifier: MIT
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// ----------------------------------------------------------------------------
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#pragma once
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namespace open3d {
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namespace t {
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namespace pipelines {
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namespace registration {
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enum class RobustKernelMethod {
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L2Loss = 0,
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L1Loss = 1,
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HuberLoss = 2,
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CauchyLoss = 3,
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GMLoss = 4,
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TukeyLoss = 5,
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GeneralizedLoss = 6,
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};
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/// \class RobustKernel
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///
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/// Base class that models a robust kernel for outlier rejection. The virtual
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/// function Weight(double residual); must be implemented in derived classes.
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/// This method will be only difference between different types of kernels and
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/// can be easily extended.
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///
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/// The kernels implemented so far and the notation has been inspired by the
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/// publication: "Analysis of Robust Functions for Registration Algorithms",
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/// Philippe Babin et al.
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///
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/// We obtain the correspondendent weights for each residual and turn the
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/// non-linear least-square problem into a IRSL (Iteratively Reweighted
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/// Least-Squares) problem. Changing the weight of each residual is equivalent
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/// to changing the robust kernel used for outlier rejection.
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///
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/// The different loss functions will only impact in the weight for each
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/// residual during the optimization step. For more information please see also:
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/// “Adaptive Robust Kernels for Non-Linear Least Squares Problems”, N.
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/// Chebrolu et al.
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/// The weight w(r) for a given residual `r` and a given loss function `p(r)` is
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/// computed as follow:
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/// w(r) = (1 / r) * (dp(r) / dr) , for all r
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/// Therefore, the only impact of the choice on the kernel is through its first
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/// order derivate.
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///
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/// GeneralizedLoss Method is an implementation of the following paper:
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/// @article{BarronCVPR2019,
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/// Author = {Jonathan T. Barron},
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/// Title = {A General and Adaptive Robust Loss Function},
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/// Journal = {CVPR},
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/// Year = {2019}
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/// }
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class RobustKernel {
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public:
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explicit RobustKernel(
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const RobustKernelMethod type = RobustKernelMethod::L2Loss,
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const double scaling_parameter = 1.0,
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const double shape_parameter = 1.0)
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: type_(type),
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scaling_parameter_(scaling_parameter),
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shape_parameter_(shape_parameter) {}
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public:
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/// Loss type.
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RobustKernelMethod type_ = RobustKernelMethod::L2Loss;
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/// Scaling parameter.
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double scaling_parameter_ = 1.0;
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/// Shape parameter.
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double shape_parameter_ = 1.0;
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};
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} // namespace registration
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} // namespace pipelines
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} // namespace t
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} // namespace open3d
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