New!!! Macaulay HC scripts available at minus/tutorial to help building your fast solver prototype.

A C++ framework capable of solving medium-sized (eg, degree > 100) square polynomial systems (ie, exactly-determined, 0-dimensional), and which is currently fastest than any other package for the CPU, notably in computer vision where trifocal minimal problems from points and lines are of importance (as in curve-based structure from motion, where lines are tangents to curves). As of this date, such problems are too high degree to be solved symbolically, while being low enough degree to allow an efficient implementation of a global technique, rather than the usual local Levenberg-Marquardt of structure from motion that requires an initialization. The solutions found using this technique for square problems can be used to initialize Levenberg-Marquardt for an overconstrained problem.

Minus is split into three parts:

• An efficient library for use in your C++ programs, no dependencies beyond standard C++ and Eigen (included)
• Simple commandline program easy to interface with other programs (eg, Matlab and Macaulay2)
• Optional: extensive tests useful for tuning the algorithm to a machine architecture and compiler. These are disabled by default, and requires VXL core library (optional - most users don't need this).

The theory and practice associated to Minus is described in

"Trifocal Relative Pose from Lines at Points and its Efficient Solution", CVPR 2020, Arxiv March 23 2019 (pdf). For datasets and curve-based SfM code, see the website.

For use in your program, we provide a C++ header-only library. Simply do:

#include <minus.hxx>
using namespace MiNuS;

And use minus<chicago> like so:

  minus<chicago>::solve(p, tgt, solutions_cameras, &nsols_final); 

to solve a trifocal pose problem from lines at points ("Chicago"), using the default formulation. See the full example in cmd/minus-chicago.cxx and tests/test-minus.cxx. This is efficient static code, so no allocations are performed.

The size and key parameters of the minimal problem are hardcoded as template parameters in advance, for efficiency. minus<> is a shorthand for a generic template, so you have full control to add your own compiled formulations, or change the floating point implementation. You can easily specify the formulation by using, e.g., minus<chicago14a> instead of minus<chicago>, which will use a 14x14 formulation for the Chicago problem. Other instances are available, e.g. minus<chicago6a> to solve a 6x6 formulation for the same problem instead.

To solve another problem, say the Cleveland trifocal pose problem from mixed points and lines, simply use the problem tag:

  minus<cleveland>::solve(p, tgt, solutions_cameras, &nsols_final);

If your measurements are in pixels, use solve_img and pass your calibration matrix K:

  minus<cleveland>::solve_img(K, p, tgt, solutions_cameras, &nsols_final);

If you want to have full control on templating, say, to change from double to float, minus<problem> is just a shorthand for minus<problem,double>. So you can use minus<chicago, float>. See the section Hacking for how to add your own problem formulation to Minus.

You will need to compile the commandline program to take advantage of processor-specific code optimization.

This requires CMake configure and make. Developer tools are required. GCC 5 to 7 is strongly recommended. You will have to have a recent version of Cmake installed (try it with your system cmake, and if it doesn't work we recommend installing Cmake from git, it is straightforward).

cd minus
ccmake .           # press 'c' repreatedly (configure), then 'g' (generate)
make

The executables are located in the bin folder (minus/bin). Each executable is optimized for a different minimal problem.

minus-chicago      # annihilates the Chicago problem!

Do an initial test

cd cmd
./minus-chicago -g         # -g profiles a predefined worst case, to get a time

Output:
  LOG Time of solver: xxxms

We will use Chicago as the basic example of a minimal problem to be solved.

The usage is as follows

minus-chicago input output 

Where input and output are ASCII text files. input encodes points and lines, and output has the solutions

If you are communicating to/from another program (Matlab or Macaulay2), you should not use physical files, but use pipes (standard input and output). Without any arguments, minus will read from stdin, and write to stdout. That way, your script can do this:

cat input | minus-chicago 
# or
minus-chicago < input > output
# In Matlab, something like this:
solutions = system( pipe input to minus-chicago )  
# solutions are output of the command that goes directly into Matlab

By default, minus reads input in a raw format with least overhead, the purpose being to use only the minus core solver from other programs, which is fastest and has the least potential for bugs, but requires the calling program to do a lot of pre-processing.

minus-chicago -i

will read input in image data measured in pixels (before inverting intrinsics K). It will read from standard input by default, which can be used with other programs with in-memory I/O, avoiding physical files:

synthdata | minus-chicago -i              # synthdata is in minus/scripts/synthdata

or

minus-chicago -i  < input > output

The input to minus-chicago -i is as follows: Input format (notation is _view_points_coords. any number of spaces and newlines optional. Can be in one row or one column as well). This input format assumes tangent data for all points, but you specify which one to use in id0 and id1 below. When --use_all_tangents is passed, will try to select the better conditioned / least degenerate tangents

  p000 p001
  p010 p011
  p020 p021
  
  p100 p101
  p110 p111
  p120 p121
  
  p100 p101
  p110 p111
  p120 p121
 
  t000 t001
  t010 t011
  t020 t021
  
  t100 t101
  t110 t111
  t120 t121
  
  t100 t101
  t110 t111
  t120 t121
  
  id0 id1           # id \in {0,1,2} of the point to consider the tangent
  
  K00 K01 K02       # intrinsic parameters: only these elements
   0  K11 K22
                    # GROUND TRUTH (optional) if -gt flag provided, pass the ground truth here:
  r000 r001 r002    # default camera format if synthcurves flag passed: 
  r010 r011 r012    # just like a 3x4 [R|T] but transposed to better fit row-major:
  r020 r021 r022    #         | R |
   c00  c01  c02    # P_4x3 = | - |
                    #         | C'|
  r100 r101 r102
  r110 r111 r112
  r120 r121 r122
   c10  c11  c12 
  
  r200 r201 r202
  r210 r211 r212
  r220 r221 r222
   c20  c21  c22

Further information is provided by typing minus-chicago --help.

For now, each time you run minus-chicago inside RANSAC, you will have to call minus again. But this is OK since you can parallelize your RANSAC using GNU Parallel. Example:

parallel minus-chicago {1} {2} ::: "$inputfiles" ::: "$outputfiles"

Will identify the number of cores and threads in your CPU and distribute a copy of minus-chicago for each input file describing a point/line configuration.

Parallel can be easily configured to run across many machines in the lab, for instance.

(I've tried running at the cluster at Brown but it seems each Xeon is far less strong than a i7 for a single run, though for parallel tasks it may overcome that. I recommend running on a couple of machines you know minus runs fast)

Their format are as follows

input:
    Encodes input points and lines in text form.
    Represented as start-target parameter pairs as P01 in solveChicago/chicago.m2 from Tim.
    The format is close to that needed by the homotopy continuation code, to
    minimize bug in brittle C++ code.  The user can write scripts to get
    user-friendly data into this format.
    
    It looks like
    
    .391195550619826 -.00262962533857666
    .310140709227333 +.169842562835882
    -.725705624433656 +.441901252816163
    .139236887010717 +.482571706362417
    -.244506857304198 +.606302490573926
    -.394166300679963 -.40618253480102
    -.195460311312153 +.426521133558775
    ....

    Which is:

    real 0 imag 0
    real 1 imag 1
    real 2 imag 2
    ...

    You will have 2*NPARAMS lines (NPARAMS = 56 for Chicago)
    

    Example in bin/P01-chicago-5lines-spherical-case1

    It can also be formatted row-wise into a 1D text array.
    
output: 
    312 solutions in Matlab text format

    It looks like 
    [1.0256116377780581939+i*0.95290270838548252197
     0.087212057713832114025+i*0.010110978306756317896
     0.048192069754345270849+i*0.03896717224674180885
     1.5403585146403313555+i*0.018243345455052351056
     ...
     -0.00074739537885242771+i*-0.0029906069387749742439]

    Notice the i multiplying the imaginary part. If you need another format,
    please let me know.

    This matrix is NSOLS by NNN, where NSOLS 312 and NNN is 14 (number of
    variables).

For developers: the start system is compiled and don't need to be input

The code:

  #include <minus.hxx>
  ...
  minus_core<chicago14a>::track(..)

Is shorthand for

  #include <minus.hxx>
  ...
  minus_core<chicago14a, double>::track(...)

where chicago14a is an enum (int) template parameter.

These (rather informal) videos provide a walkthrough on how to add a new minimal problem to MINUS:

How to add a new problem - Part 1 https://youtu.be/Yp5n6me04-Y

How to add a new problem - Part 2 https://youtu.be/KlSxoWQ0SdA

Let's say you have a new minimal problem, Chicago 6a, that is, variant a to the 6x6 formulation of the trifocal pose from points at lines problem. You might want to do use this since a small number of variables can mean a drastically more efficient linear solve during solution tracking.

Steps:

• Include a new name chicago6a for your problem in the enum problem in the beginning of minus.h. This is the table of problem tags of Minus.
• Specify the constant parameters for the problem and formulation:
  • In the file parameters.h, write another include line for your problem:

#include<chicago6a.h>

- Write the header `chicago6a.h` by copying chicago14a.h and filling in the
  numbers and substituting the string `chicago14a` to `chicago6a`. You can add any constants
  that you feel are needed for your particular problem. For example, if your
  minmimal problem involves conics, you might want to add the number of conics.
  If your minimal problem formulation has multiple start solutions, for speed,
  you might want to indicate that as well. These are the solver settings that are
  known at compile time and can't be changed at runtime.

• Place your evaluation functions into a file called chiago6a.hxx. using the existing file chicago14a.hxx to see how the function should be defined. Basically, you need to copy what is in chicago14a.hxx, include your function bodies according to the format, and substitute chicago14a to chicago6a.
  • Negate y entries by hand
  • X are refs: %s/C X/const C<F> \&X/g
  • Consts C are constexprs: %s/C \(C[0-9] =\)/static constexpr C<F> \1/gc
  • Gates G are variables %s/C G/const C<F> G/gc for gates (no ampersand)
  • At the end of minus.hxx, copy and paste the last include line to your problem, eg: #include <minus/chicago6a.hxx>
• Optional: create your app in cmd/ immitating cmd/minus-chicago.cxx. If you have just added a new formulation solver for the same problem, make the necessary additions to the existing app, e.g., cmd/minus-chicago.cxx.
• Optional: Default start system and gammified parameters. You might want to define a default start system. you might also want to define default gammified parameters to test your solver independent of I/O functions to convert from user input to gammified homotopy parameters.
  • mimmick the file chicago14a-default.hxx to create your own, e.g., chicago6a-default.hxx.
  • if your start system comes from our Macaulay2 scripts, use the commands in scripts/extract-start-sols.vim to help you translate to C++ vector initialization format
  • in your app, say cmd/minus-chicago.cxx for chicago problems, for now you have to selectively include this. Ongoing work will remove this need, being only needed to do solver.
• Optional: place a define to simplify your solver name: this is a using clause towards the end of minus.h.
• Optional: If you are using Minus header-only, you are done! But for faster compile times and for including your formulation on the the official Minus codebase, and for smaller codes, you can use our libminus, with an explicit instantiation. In Templates/minus-chicago-alltypes-allformulations+double.cxx, copy the line starting with template class and place your own instantiation. If it is a different problem than Chicago, you may want to place it in a similar file with chicago in the name.

Enable tests in cmake, then indicate where your vxl build folder is located. When building VXL, only build VNL.

Most advanced programming tools work best under Linux.

[https://github.com/google/sanitizers/wiki/AddressSanitizer]

You can build release with it, no need for slow debug. This is very fast. Highly recommended for developing efficient code using vectors, pointers and buffers

Add this to MINUS_EXTRA_CMAKE_CXX_FLAGS:

Now recompile minus and simply run it. If nothing happens, you're golden. In the event of any memleak, there will be a colorful output showing where it came from, specially under Linux.

The best way is with kcachegrind + valgrind, by far. See [https://www.blogger.com/comment.g?blogID=7395958&postID=116062684092668856&bpli=1&pli=1]

If no valgrind or kcachegrind is available (eg, some Macs), the easiest way is with gprof

Expect your program to take very very long - so maybe reduce the problem / iterations before running.

Mac OS: Follow these intructions to install valgrind. https://github.com/LouisBrunner/valgrind-macos

Install qcachegrind brew install qcachegrind

Use valgrind --tool=callgrind minus-chicago -g

kcachegrind (or qcachegrind)

https://developer.ridgerun.com/wiki/index.php/Profiling_with_GPerfTools https://github.com/gperftools/gperftools https://gperftools.github.io/gperftools/cpuprofile.html

Runs the program on real CPU rather than a simulation. This is a surprisingly fast profiler. Basically no overhead.

To use it, install it with homebrew, then link the program with libprofiler (add -Lpath_to_installed_gperftools -lprofiler) and run with CPUPROFILE environment variable set to some filename

CPUPROFILE=/tmp/prof ./minus-chicago -g

I use -L/usr/loca/lib -lprofiler on MINUS_EXTRA_CMAKE_EXE_LINKER_FLAGS

It will profile the program using interval timer (setitimer) and output profiling data to the filename, defined in CPUPROFILE env var. Then you can view profile data in command-line or with svg/web browser using pprof perl script from gperftools (pprof ./the_program profile01).

pprof --pdf ./minus-chicago /tmp/prof >/tmp/p.pdf

mkdir /tmp/out
xctrace record --output /tmp/out --template "Time Profiler" --launch --  minus-chicago -g
open /tmp/out/*

This was extensively tested with GCC 5 Do not use GCC 4 or 8

Intel ICC compiler with the same optimization flags as usual in Minus provided a 2x DECREASE in speed. TODO: try other ICC-specific optimization flags

Also no success with GCC8 - slow or breaks fastmath.

Some versions of GCC might not detect your processor correctly. We use -march=native but if your processor is newer relative to your GCC, then you might have to confirm:

 gcc -march=native -Q --help=target | grep -- '-march=' | cut -f3

For gcc 5, this returns broadwell, when my arch is kabylake which is compatible to skylake, so gcc should be detecting as skylake ideally. Using newer GCC guarantees that for me. You can also try

gcc-8 -march=native -E -v - </dev/null 2>&1 | grep cc1

To inspect the flags that are implied. I got good perf with gcc5, but it was using by default -mtune=generic. If I put -mtune=skylake it might speedup further.

To see command-line flags implied by eg -march=native, use:

gcc -march=native -E -v - </dev/null 2>&1 | grep cc1

If you want to see the compiler/precompiler defines set by certain parameters, do this:

echo | gcc -dM -E - -march=native

Simply set the CC and CXX flags on the cmake step. Example:

CC=gcc CXX=g++ ccmake .

or, e.g.,

CC=gcc-7 CXX=g++-7 ccmake .

and so on.

Press 't' in ccmake to make sure all compiler-related paths are to the desired compiler. If you have too many compilers around, you might want to force PATH like so:

CC=/usr/bin/cc CXX=/usr/bin/c++ PATH=/usr/bin:$PATH ccmake .

Assuming you want to use the compiler at /usr/bin.

Some tests were carried out with Intel ICC, but the gains were not significant enough to justify a proprietary compiler.

At Brown's CCV cluster, I used the following flags:

 MINUS_EXTRA_CMAKE_CXX_FLAGS	  -I${MKLROOT}/include -no-prec-div -ansi-alias  -mskylake-avx512 -xSKYLAKE-AVX512 -axSKYLAKE-AVX512
 MINUS_EXTRA_CMAKE_EXE_LINKER_FLAGS   -L/gpfs/runtime/opt/intel/2018.1/mkl/lib/intel64_lin   -lmkl_intel_lp64 -lmkl_sequential -lmkl_core -lpthread -lm

https://www.naftaliharris.com/blog/2x-speedup-with-one-line-of-code/

Linux: pahole macOS: clang -Wpadded

We refer to Eigen's documentation http://eigen.tuxfamily.org/index.php?title=Developer%27s_Corner#Studying_assembly_output

For SSE/VMX use, good examples include: Eigen/src/LU/arch/Inverse_SSE.h As well as VXL's VNL

FMA could potentially speedup the evaluators.

Place this between code:

   asm("#------ BEGIN!");
   // some code to analyze
   asm("#------ END!");

use/adapt the script scripts/minus-disassemble

Source code adapted and improved over Anton Leykin's and Timothy Duff's codes from Macaulay2 by Ricardo Fabbri. The core code grew out of Macaulay2/e/NAG.cpp. These improvements and specializations were jointly developed with Tomas Pajdla, Benjamin Kimia and Hongyi Fan, with intensive discussion and testing from remaining Arxiv paper co-authors while at ICERM/Brown University: Jonathan Hauenstein, Margaret Regan, Elias Tsigaridas, Charles Wrampler, and David da Costa de Pinho. The Chicago problem was originally formulated as a differential version of curve-based trifocal estimation by Ricardo Fabbri, Peter Giblin, and Benjamin Kimia.

Minus was born out of Brown University/ICERM's 2018 Nonlinear Algebra Program (Computer Vision Working group) and the 2019 Algebraic Vision research cluster, the former co-organized by the authors Leykin, Hauenstein and the latter by Fabbri. Minus received an NSF research highlight.

• ``MiNuS + GPU'': https://github.com/C-H-Chien/Homotopy-Continuation-Tracker-on-GPU
• ``MiNuS + machine learning for optimizing certain problems:'' https://github.com/petrhruby97/learning_minimal
• ``MiNuS + quadrifocal geometry:'' https://github.com/petrhruby97/radial_quadrifocal
• PS: These titles are an over-simplification of the linked papers