BundleAdjustmentModels documentation

Julia repository of bundle adjustment problems from the Bundle Adjustment in the Large repository

How to Cite

If you use BundleAdjustmentModels.jl in your work, please cite using the format given in CITATION.cff.

Tutorial

Check an Introduction to BundleAdjustmentModels and more tutorials on the JSO tutorials page.

Bug reports and discussions

If you think you found a bug, feel free to open an issue. Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.

If you want to ask a question not suited for a bug report, feel free to start a discussion here. This forum is for general discussion about this repository and the JuliaSmoothOptimizers, so questions about any of our packages are welcome.

Examples

using BundleAdjustmentModels, DataFrames

problems_df() returns a DataFrame of all the problems, their group and other features.

df = problems_df()

When you get this dataframe you can sort through it to get the problems that you want. For example, if you want to filter problems based on their size you can apply this filter:

filter_df = df[ ( df.nequ .≥ 50000 ) .& ( df.nvar .≤ 34000 ), :]

You can get the problem name directly from the dataframe:

name = filter_df[1, :name]

"problem-49-7776"

fetch_ba_name returns the path to the problem artifact. The artifact will download automatically:

path = fetch_ba_name(name)

"/home/runner/.julia/artifacts/1c889d92a544c32de78828666f6b4d025495945b"

You can also get an array of the paths to an entire group of problems

path = fetch_ba_group("ladybug")

30-element Vector{String}:
 "/home/runner/.julia/artifacts/1c889d92a544c32de78828666f6b4d025495945b"
 "/home/runner/.julia/artifacts/4461ac81e3c3c5793507c377bc719b421ece44ca"
 "/home/runner/.julia/artifacts/658f23b6973c0f3d927ec4af548616781e333dd5"
 "/home/runner/.julia/artifacts/59f166b21f6dc628a4b8d06f10846ef223c3398f"
 "/home/runner/.julia/artifacts/6cea78f2387bc93d89a74570f7c4b88f9e1c9eb5"
 "/home/runner/.julia/artifacts/922cbd8236181aad6958c3c47fce6205c27fb88d"
 "/home/runner/.julia/artifacts/e8560260787f9fd681ee45b0fcbf5fc4b46850dd"
 "/home/runner/.julia/artifacts/ed4ea0afac50c71bbeadd1843c0a45d0cdc564c9"
 "/home/runner/.julia/artifacts/63f60e2b50589f60f8cf42dfe7e4541311cf2e5f"
 "/home/runner/.julia/artifacts/759168bc1230360398ccead443f455e7ed498b85"
 ⋮
 "/home/runner/.julia/artifacts/03e592564dc0f42b5eedab7a3b53bbf9ecbad3e1"
 "/home/runner/.julia/artifacts/4b4f58c073570eede8d9bc0e18031a2985b10195"
 "/home/runner/.julia/artifacts/fc39bfe440d138e8bf9adc442e45a1975f8fb46f"
 "/home/runner/.julia/artifacts/580bfe822e16d9a789516df19abb0d6fb61538ed"
 "/home/runner/.julia/artifacts/deee0e713f9b46cd9a75f245d823fde043a89c1a"
 "/home/runner/.julia/artifacts/cc4ba6b13ab362d4c305d2f21c06cb10d4a4fd90"
 "/home/runner/.julia/artifacts/2b08e18ae0bb7d65709a0e062fe937b0f76af099"
 "/home/runner/.julia/artifacts/481a3d5abd3f7fe09e0478fd9f0923e2802955ea"
 "/home/runner/.julia/artifacts/4262d69f73c4e228881886f5403f68034d41588f"

You can directly construct a nonlinear least-squares model based on NLPModels:

model = BundleAdjustmentModel("problem-49-7776")

BundleAdjustmentModel{Float64, Vector{Float64}}
  Problem name: problem-49-7776
   All variables: ████████████████████ 23769  All constraints: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0        All residuals: ████████████████████ 63686 
            free: ████████████████████ 23769             free: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                lower: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0            nonlinear: ████████████████████ 63686 
           upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                upper: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 nnzj: ( 99.95% sparsity)   764232
         low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0              low/upp: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 nnzh: (100.00% sparsity)   0     
           fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                fixed: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
          infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               infeas: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
            nnzh: (  0.00% sparsity)   282494565          linear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
                                                    nonlinear: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
                                                         nnzj: (------% sparsity)         

  Counters:
             obj: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 grad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 cons: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
        cons_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0             cons_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 jcon: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           jgrad: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                  jac: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0              jac_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
         jac_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                jprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0            jprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
       jprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jtprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0           jtprod_lin: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
      jtprod_nln: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                 hess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0                hprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
           jhess: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0               jhprod: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0             residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
    jac_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0       jprod_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0      jtprod_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     
   hess_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0       jhess_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0       hprod_residual: ⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅⋅ 0     

You can then evaluate the residual and jacobian (or their in-place version) from NLPModels:

using NLPModels

residual(model, model.meta.x0)

63686-element Vector{Float64}:
  332.6499999997587
 -262.08999999980693
  199.759999999918
 -166.69999999993001
  253.05999999988057
 -202.26999999990284
  -58.13000000000665
 -271.8900000000313
 -238.22000000002816
 -237.37000000002828
    ⋮
   19.23998999999935
  379.0699999997969
  -43.83001999997662
 -111.21999999999963
  -36.46996999999988
  281.63999999991705
  -24.150019999992747
 -202.19999999999803
  -26.349979999999746

meta_nls = nls_meta(model)
Fx = similar(model.meta.x0, meta_nls.nequ)
residual!(model, model.meta.x0, Fx)

63686-element Vector{Float64}:
  332.6499999997587
 -262.08999999980693
  199.759999999918
 -166.69999999993001
  253.05999999988057
 -202.26999999990284
  -58.13000000000665
 -271.8900000000313
 -238.22000000002816
 -237.37000000002828
    ⋮
   19.23998999999935
  379.0699999997969
  -43.83001999997662
 -111.21999999999963
  -36.46996999999988
  281.63999999991705
  -24.150019999992747
 -202.19999999999803
  -26.349979999999746

You need to call jac_structure_residual! at least once before calling jac_op_residual!.

meta_nls = nls_meta(model)
rows = Vector{Int}(undef, meta_nls.nnzj)
cols = Vector{Int}(undef, meta_nls.nnzj)
jac_structure_residual!(model, rows, cols)

([1, 1, 1, 1, 1, 1, 1, 1, 1, 1  …  63686, 63686, 63686, 63686, 63686, 63686, 63686, 63686, 63686, 63686], [1, 2, 3, 23329, 23330, 23331, 23332, 23333, 23334, 23335  …  23328, 23761, 23762, 23763, 23764, 23765, 23766, 23767, 23768, 23769])

You need to call jac_coord_residual! everytime before calling jac_op_residual!.

vals = similar(model.meta.x0, meta_nls.nnzj)
jac_coord_residual!(model, model.meta.x0, vals)

764232-element Vector{Float64}:
  8.518413331982776e-10
 -3.9092363053433497e-10
 -1.0015997648064811e-9
 -1.2978518096352338e-9
 -2.181461711333818e-9
 -2.732794216648834e-10
  8.629276559090283e-10
 -3.789683472039958e-10
 -9.966924717285587e-10
 -6.023460204030549e-13
  ⋮
  1.2440981702971967e-11
 -4.2515116698719585e-12
  6.176844071113596e-12
  7.395811310180752e-13
  3.0572174355775086e-12
  5.663375609968155e-13
  6.169318993032598e-16
  1.5716458219608994e-16
  6.662548507594127

Jv = similar(model.meta.x0, meta_nls.nequ)
Jtv = similar(model.meta.x0, meta_nls.nvar)
Jx = jac_op_residual!(model, rows, cols, vals, Jv, Jtv)

Linear operator
  nrow: 63686
  ncol: 23769
  eltype: Float64
  symmetric: false
  hermitian: false
  nprod:   0
  ntprod:  0
  nctprod: 0

There is no second order information available for problems in this module.

Delete unneeded artifacts and free up disk space with delete_ba_artifact!:

delete_ba_artifact!("problem-49-7776-pre")

[ Info: The artifact ladybug/problem-49-7776-pre.txt.bz2 has been deleted

Use delete_all_ba_artifacts! to delete all artifacts:

delete_all_ba_artifacts!()

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Licensed under the MPL-2.0 License