push rpose v1

Author: simonwebertum

CMakeLists.txt

@@ -4,7 +4,7 @@ message(STATUS "CMake version: ${CMAKE_VERSION}")
 
 include(cmake/PreventInSourceBuild.cmake)
 
-project(rootba VERSION 0.1.0 LANGUAGES CXX)
+project(rootba_povar VERSION 0.1.0 LANGUAGES CXX)
 
 
 # helper
@@ -104,16 +104,4 @@ include(cmake/SetupDependencies.cmake)
 
 add_subdirectory(external)
 
-if(ROOTBA_ENABLE_TESTING)
-  message(STATUS "Testing is enabled")
-  # enable before src, which also contains unit tests
-  enable_testing()
-  # for gtest_discover_test
-  include(GoogleTest)
-endif()
-
-add_subdirectory(src)
-
-if(ROOTBA_ENABLE_TESTING)
-  add_subdirectory(test)
-endif()
+add_subdirectory(src)

README.md

@@ -1,51 +1,119 @@
-# RootBA: Square Root Bundle Adjustment
-
-[Project Page](https://go.vision.in.tum.de/rootba) | 
-[Paper](https://arxiv.org/abs/2103.01843) | 
-[Poster](https://vision.in.tum.de/_media/research/vslam/rootba/demmel2021cvpr_rootba_poster.pdf) |
-[Video](https://youtu.be/kAhmjNL8B-U) |
-[Code](https://github.com/NikolausDemmel/rootba)
-
-<img src="docs/images/teaser.jpg" alt="teaser image" />
-
-## Table of Contents
-
-* [Citation](#citation)
-* [Dependencies](#dependencies)
-  * [Installing dependencies on Linux](#installing-dependencies-on-linux)
-  * [Installing depedencies on macOS](#installing-depedencies-on-macos)
-* [Building](#building)
-  * [CMake Options](#cmake-options)
-  * [Running Unit Tests](#running-unit-tests)
-* [BAL Problems](#bal-problems)
-* [Testing Bundle Adjustment](#testing-bundle-adjustment)
-  * [Visualization of BAL Problems](#visualization-of-bal-problems)
-  * [Running Bundle Adjustment](#running-bundle-adjustment)
-  * [Config Options](#config-options)
-  * [Visualization of Results](#visualization-of-results)
-  * [Batch Evaluation](#batch-evaluation)
-* [Repository Layout](#repository-layout)
-  * [Code Layout](#code-layout)
-* [License](#license)
+# Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment, ECCV 2024
 
-## Citation
+Welcome to the official page of the paper [Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment](https://arxiv.org/pdf/2405.05079).
+
+## Open-Source Implementation
+
+### Install Dependencies and Build External Libraries
+
+This implementation is built on [RootBA](https://github.com/NikolausDemmel/rootba). Please follow the same instructions to install the dependencies, as well as to build the external libraries.
+
+### Build PoVar
+
+**Build PoVar option a)**
+
+Use the build script.
+
+```
+./scripts/build-rootba-povar.sh [BUILD_TYPE]
+```
+
+You can optionally pass the cmake `BUILD_TYPE` used to compile RootBA
+as the first argument. If you don't pass anything the default is
+`Release`. The cmake build folder is `build`, inside the project
+root. This build script will use `ccache` and `ninja` automaticaly if
+they are found on `PATH`.
+
+**Build PoVar option b)**
+
+Manually build with the standard cmake workflow.
+
+```
+mkdir build && cd build
+cmake ..
+make -j8
+```
+
+The cmake project will automatically use `ccache` if it is found on
+`PATH` (unless you override by manually specifying
+`CMAKE_C_COMPILER_LAUNCHER`/`CMAKE_CXX_COMPILER_LAUNCHER`). To use
+`ninja` instead of `make`, you can use:
+
+```
+cmake .. -G Ninja
+ninja
+```
+
+### Running PoVar
+
+#### Dataset
+
+You can download the BAL dataset on the [Bundle Adjustment in the Large](https://grail.cs.washington.edu/projects/bal/) webpage. 
+Before using our solver, we randomly initialize a projective camera model, with the option ```--create-dataset```:
+```
+./bin/bal --input /venice/problem-89-110973-pre.txt --create-dataset true
+```
+
+#### Solving initialization-free stratified BA
+
+In line with [our paper](https://arxiv.org/abs/2405.05079), the stratified BA includes two steps.
+
+(a) We propose four different solvers ```--solver-type-step-1``` for the nonlinear separable optimization problem:
+* ```POWER_VARPROJ```: Variable projection with power series expansion (by default)
+* ```POWER_BUNDLE_ADJUSTMENT```: Levenberg-Marquardt with power series expansion (see PoBA https://arxiv.org/abs/2204.12834)
+* ```PCG```: Variable projection with preconditioned conjugate gradients
+* ```CHOLESKY```: Variable projection with Cholesky factorization
+
+
+(b) We propose two different solvers ```--solver-type-step-2``` for the projective refinement problem:
+
+* ```RIPOBA```: Riemannian manifold framework for Levenberg-Marquardt with power series (by default)
+* ```RIPCG```: Riemannian manifold framework for Levenberg-Marquardt with preconditioned conjugate gradients
 
-If you find our work useful in your research, please consider citing:
 
+The implementation uses ```double``` precision.
+
+#### Command line
+Once the random initialization has been done, you can run the two solvers in a row with, for instance:
+```
+./bin/bal --num-threads 4 --input /data_custom/venice/problem-89-110973-pre.txt --solver-type-step-1 POWER_SCHUR_COMPLEMENT --solver-type-step-2 RIPOBA 
 ```
-@inproceedings{demmel2021rootba,
- author = {Nikolaus Demmel and Christiane Sommer and Daniel Cremers and Vladyslav Usenko},
- title = {Square Root Bundle Adjustment for Large-Scale Reconstruction},
+
+The command ```--help``` will provide some explanations about the different options.
+In particular, you can set, among others:
+* ```--max-num-iterations```: maximum number of outer iterations for each step (by default, 50).
+* ```--power-sc-iterations```: maximum order of power series (by default, 20).
+* ```--residual-robust-norm```: NONE (by default), CAUCHY, HUBER.
+* ```--alpha```: weight of the affine part in pOSE formulation (by default, 0.1).
+
+
+
+## Abstract
+
+Most Bundle Adjustment (BA) solvers like the Levenberg- Marquardt algorithm require a good initialization. Instead, initialization-free BA remains a largely uncharted territory. The under-explored Variable Projection algorithm (VarPro) exhibits a wide convergence basin even without initialization. Coupled with object space error formulation, recent works have shown its ability to solve small-scale initialization-free bundle adjustment problem. To make such initialization-free BA approaches scalable, we introduce Power Variable Projection (PoVar), extending a recent inverse expansion method based on power series. Importantly, we link the power series expansion to Riemannian manifold optimization. This projective framework is crucial to solve large-scale bundle adjustment problems without initialization. Using the real-world BAL dataset, we experimentally demonstrate that our solver achieves state-of-the-art results in terms of speed and accuracy. To our knowledge, this work is the first to address the scalability of BA without initialization opening new venues for initialization-free structure-from-motion.
+
+## Citation
+If you find our work useful in your research, please consider citing:
+
+```bibtex
+@article{weber2024power,
+  title={Power Variable Projection for Initialization-Free Large-Scale Bundle Adjustment},
+  author={Weber, Simon and Hong, Je Hyeong and Cremers, Daniel},
+  journal={arXiv preprint arXiv:2405.05079},
+  year={2024}
+}
+
+@inproceedings{weber2023poba,
+ author = {Simon Weber and Nikolaus Demmel and Tin Chon Chan and Daniel Cremers},
+ title = {Power Bundle Adjustment for Large-Scale 3D Reconstruction},
  booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
- year = {2021}
+ year = {2023}
 }
 ```
 
-> *Note:* The initial public release in this repository corresponds to
-> the code version evluated in the CVPR'21 paper, after refactoring
-> and cleanup. Except for minor numerical differences, the results
-> should be reproducible on comparable hardware. As the code evolves,
-> runtime differences might become larger.
+
+
+
 
 ## Dependencies