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Keir Mierle8ebb0732012-04-30 23:09:08 -07001// Ceres Solver - A fast non-linear least squares minimizer
2// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
3// http://code.google.com/p/ceres-solver/
4//
5// Redistribution and use in source and binary forms, with or without
6// modification, are permitted provided that the following conditions are met:
7//
8// * Redistributions of source code must retain the above copyright notice,
9// this list of conditions and the following disclaimer.
10// * Redistributions in binary form must reproduce the above copyright notice,
11// this list of conditions and the following disclaimer in the documentation
12// and/or other materials provided with the distribution.
13// * Neither the name of Google Inc. nor the names of its contributors may be
14// used to endorse or promote products derived from this software without
15// specific prior written permission.
16//
17// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
18// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
19// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
20// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
21// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
22// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
23// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
24// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
25// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
26// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
27// POSSIBILITY OF SUCH DAMAGE.
28//
29// Author: keir@google.com (Keir Mierle)
30// sameeragarwal@google.com (Sameer Agarwal)
31
32#ifndef CERES_PUBLIC_LOCAL_PARAMETERIZATION_H_
33#define CERES_PUBLIC_LOCAL_PARAMETERIZATION_H_
34
35#include <vector>
36#include "ceres/internal/port.h"
37
38namespace ceres {
39
40// Purpose: Sometimes parameter blocks x can overparameterize a problem
41//
42// min f(x)
43// x
44//
45// In that case it is desirable to choose a parameterization for the
46// block itself to remove the null directions of the cost. More
47// generally, if x lies on a manifold of a smaller dimension than the
48// ambient space that it is embedded in, then it is numerically and
49// computationally more effective to optimize it using a
50// parameterization that lives in the tangent space of that manifold
51// at each point.
52//
53// For example, a sphere in three dimensions is a 2 dimensional
54// manifold, embedded in a three dimensional space. At each point on
55// the sphere, the plane tangent to it defines a two dimensional
56// tangent space. For a cost function defined on this sphere, given a
57// point x, moving in the direction normal to the sphere at that point
58// is not useful. Thus a better way to do a local optimization is to
59// optimize over two dimensional vector delta in the tangent space at
60// that point and then "move" to the point x + delta, where the move
61// operation involves projecting back onto the sphere. Doing so
62// removes a redundent dimension from the optimization, making it
63// numerically more robust and efficient.
64//
65// More generally we can define a function
66//
67// x_plus_delta = Plus(x, delta),
68//
69// where x_plus_delta has the same size as x, and delta is of size
70// less than or equal to x. The function Plus, generalizes the
71// definition of vector addition. Thus it satisfies the identify
72//
73// Plus(x, 0) = x, for all x.
74//
75// A trivial version of Plus is when delta is of the same size as x
76// and
77//
78// Plus(x, delta) = x + delta
79//
80// A more interesting case if x is two dimensional vector, and the
81// user wishes to hold the first coordinate constant. Then, delta is a
82// scalar and Plus is defined as
83//
84// Plus(x, delta) = x + [0] * delta
85// [1]
86//
87// An example that occurs commonly in Structure from Motion problems
88// is when camera rotations are parameterized using Quaternion. There,
89// it is useful only make updates orthogonal to that 4-vector defining
90// the quaternion. One way to do this is to let delta be a 3
91// dimensional vector and define Plus to be
92//
93// Plus(x, delta) = [cos(|delta|), sin(|delta|) delta / |delta|] * x
94//
95// The multiplication between the two 4-vectors on the RHS is the
96// standard quaternion product.
97//
98// Given g and a point x, optimizing f can now be restated as
99//
100// min f(Plus(x, delta))
101// delta
102//
103// Given a solution delta to this problem, the optimal value is then
104// given by
105//
106// x* = Plus(x, delta)
107//
108// The class LocalParameterization defines the function Plus and its
109// Jacobian which is needed to compute the Jacobian of f w.r.t delta.
110class LocalParameterization {
111 public:
112 virtual ~LocalParameterization() {}
113
114 // Generalization of the addition operation,
115 //
116 // x_plus_delta = Plus(x, delta)
117 //
118 // with the condition that Plus(x, 0) = x.
119 virtual bool Plus(const double* x,
120 const double* delta,
121 double* x_plus_delta) const = 0;
122
123 // The jacobian of Plus(x, delta) w.r.t delta at delta = 0.
124 virtual bool ComputeJacobian(const double* x, double* jacobian) const = 0;
125
126 // Size of x.
127 virtual int GlobalSize() const = 0;
128
129 // Size of delta.
130 virtual int LocalSize() const = 0;
131};
132
133// Some basic parameterizations
134
135// Identity Parameterization: Plus(x, delta) = x + delta
136class IdentityParameterization : public LocalParameterization {
137 public:
138 explicit IdentityParameterization(int size);
139 virtual ~IdentityParameterization() {}
140 virtual bool Plus(const double* x,
141 const double* delta,
142 double* x_plus_delta) const;
143 virtual bool ComputeJacobian(const double* x,
144 double* jacobian) const;
145 virtual int GlobalSize() const { return size_; }
146 virtual int LocalSize() const { return size_; }
147
148 private:
149 const int size_;
150};
151
152// Hold a subset of the parameters inside a parameter block constant.
153class SubsetParameterization : public LocalParameterization {
154 public:
155 explicit SubsetParameterization(int size,
156 const vector<int>& constant_parameters);
157 virtual ~SubsetParameterization() {}
158 virtual bool Plus(const double* x,
159 const double* delta,
160 double* x_plus_delta) const;
161 virtual bool ComputeJacobian(const double* x,
162 double* jacobian) const;
163 virtual int GlobalSize() const { return constancy_mask_.size(); }
164 virtual int LocalSize() const { return local_size_; }
165
166 private:
167 const int local_size_;
168 vector<int> constancy_mask_;
169};
170
171// Plus(x, delta) = [cos(|delta|), sin(|delta|) delta / |delta|] * x
172// with * being the quaternion multiplication operator. Here we assume
173// that the first element of the quaternion vector is the real (cos
174// theta) part.
175class QuaternionParameterization : public LocalParameterization {
176 public:
177 virtual ~QuaternionParameterization() {}
178 virtual bool Plus(const double* x,
179 const double* delta,
180 double* x_plus_delta) const;
181 virtual bool ComputeJacobian(const double* x,
182 double* jacobian) const;
183 virtual int GlobalSize() const { return 4; }
184 virtual int LocalSize() const { return 3; }
185};
186
187} // namespace ceres
188
189#endif // CERES_PUBLIC_LOCAL_PARAMETERIZATION_H_