Merge pull request #5 from arfon/patch-1

Minor paper updates

paper.md

@@ -15,14 +15,20 @@ date: 27 February 2020
 bibliography: paper.bib
 ---
 
+# Statement of Need
+
+Synthetic datasets have zero error and so are useful in testing the accuracy and correctness of different methods.
+To test different algorithms, the ground truth datasets need some form of added error.
+Currently, there's a lack of tools for adding errors to simulate different inaccuracies in Structure from Motion (SfM) methods. 
+**City2BA** aims to address this need by providing variety of different types of error.
+
 # Summary
 
-Bundle adjustment is a global nonlinear optimization step used in structure from motion (SfM) and simultaneous localization and mapping (SLAM).
+Bundle adjustment is a global nonlinear optimization step used in SfM and simultaneous localization and mapping (SLAM).
 It is usually formulated as a nonlinear least-squares problem where the goal is to minimize the error between the projected location of 3D points in each camera and the actual observed location of the point in the camera frames.
 @triggs1999bundle provides a good overview of the formulation and uses of bundle adjustment.
 For SLAM, the bundle adjustment problem is small, but for SfM, problem sizes can grow very large.
-Ideally, developers of new bundle adjustment algorithms would like to test against real world data.
-However, not many datasets are available (the authors only know of [1DSFM](http://www.cs.cornell.edu/projects/1dsfm/) [@wilson2014robust] and [Bundle Adjustment in the Large](http://grail.cs.washington.edu/projects/bal/) [@agarwal2010bundle]), and these datasets are limited in size and structure.
+Ideally, developers of new bundle adjustment algorithms would like to test against real world data however not many datasets are available (the authors only know of [1DSFM](http://www.cs.cornell.edu/projects/1dsfm/) [@wilson2014robust] and [Bundle Adjustment in the Large](http://grail.cs.washington.edu/projects/bal/) [@agarwal2010bundle]), and these datasets are limited in size and structure.
 We know large datasets exists and are in use (see @klingner2013street), but these datasets are not available to the public.
 To facilitate the development of bundle adjustment algorithms, we developed the **City2BA** package which can generate large, synthetic bundle adjustment datasets.
 
@@ -36,15 +42,4 @@ Cameras can either be generated in a streetview-like scenario where cameras are
 Currently, **City2BA** ships with a single camera model commonly used in the literature (see @agarwal2010bundle), but the user can add their own camera model if they so choose.
 It is optimized and can generate very large models (100,000 cameras, 1,000,000) in less than an hour.
 
-# Statement of Need
-
-Synthetic datasets have zero error and so are useful in testing the accuracy and correctness of different methods.
-To test different algorithms, the ground truth datasets need some form of added error.
-The tools we provide can add a variety of different types of error to simulate different inaccuracies in SfM methods.
-For example, we provide long range drifting effects that mimic error from accelerometer noise.
-These features allow users to test the scalability of their algorithm over a variety of scenarios and types of noise.
-
-
-
-
 # References