revise paper

docs/paper.bib

@@ -59,3 +59,61 @@ @inproceedings{Segal
   year={2009},
   doi={10.15607/rss.2009.v.021}
 }
+
+@INPROCEEDINGS{Wang,
+  author={Wang, Han and Wang, Chen and Xie, Lihua},
+  booktitle={IEEE International Conference on Robotics and Automation (ICRA2020)}, 
+  title={Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection}, 
+  year={2020},
+  pages={2095-2101},
+  doi={10.1109/ICRA40945.2020.9196764}
+}
+
+@inproceedings{Izadinia,
+  title={Scene recomposition by learning-based icp},
+  author={Izadinia, Hamid and Seitz, Steven M},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={930--939},
+  year={2020}
+}
+
+@ARTICLE{Kim,
+  author={Kim, Kyuwon and Im, Junhyuck and Jee, Gyuin},
+  journal={IEEE Transactions on Intelligent Transportation Systems}, 
+  title={Tunnel Facility Based Vehicle Localization in Highway Tunnel Using 3D LIDAR}, 
+  year={2022},
+  volume={23},
+  number={10},
+  pages={17575-17583},
+  doi={10.1109/TITS.2022.3160235}
+}
+
+@article{Pomerleau,
+	author = {Pomerleau, Fran{\c c}ois and Colas, Francis and Siegwart, Roland and Magnenat, St{\'e}phane},
+	title = {{Comparing ICP Variants on Real-World Data Sets}},
+	journal = {Autonomous Robots},
+	year = {2013},
+	volume = {34},
+	number = {3},
+	pages = {133--148},
+	month = feb,
+  doi={10.1007/s10514-013-9327-2}
+}
+
+@INPROCEEDINGS{Serafin,
+  author={Serafin, Jacopo and Grisetti, Giorgio},
+  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS2015)}, 
+  title={NICP: Dense normal based point cloud registration}, 
+  year={2015},
+  pages={742-749},
+  doi={10.1109/IROS.2015.7353455}}
+
+
+@inproceedings{Datar,
+author = {Datar, Mayur and Immorlica, Nicole and Indyk, Piotr and Mirrokni, Vahab S.},
+title = {Locality-sensitive hashing scheme based on p-stable distributions},
+year = {2004},
+doi = {10.1145/997817.997857},
+booktitle = {Twentieth Annual Symposium on Computational Geometry},
+pages = {253–262},
+}

docs/paper.md

@@ -20,12 +20,14 @@ bibliography: paper.bib
 
 Point cloud registration is a task of aligning two point clouds measured by 3D ranging
 sensors, for example, LiDARs and range cameras. Iterative point cloud registration,
-also known as fine registration or local registration, is particularly crucial. This
-process iteratively performs proximity-based point correspondence search and minimizes
-the distance between corresponding points. Iterative closest point (ICP) and its variants,
+also known as fine registration or local registration, iteratively refines the transformation
+between point clouds starting from an initial guess.
+Each iteration involves a proximity-based point correspondence search and the minimization
+of the distance between corresponding points, continuing until convergence.
+Iterative closest point (ICP) and its variants,
 such as Generalized ICP, are representative iterative point cloud registration algorithms.
-They are widely used in applications like autonomous vehicle localization, place recognition,
-and object classification. Since these applications often require real-time or near-real-time
+They are widely used in applications like autonomous vehicle localization [@Kim], place recognition [@Wang],
+and object classification [@Izadinia]. Since these applications often require real-time or near-real-time
 processing, speed is a critical factor in point cloud registration routines.
 
 **small_gicp** provides efficient and parallel algorithms to create an extremely
@@ -37,8 +39,9 @@ to offer efficiency, portability, and customizability.
 
 # Statement of need
 
-There are several point cloud processing libraries, and PCL [@Rusu] and Open3D
-[@Zhou] are the notable ones among them.
+There are several point cloud processing libraries, and PCL [@Rusu], Open3D
+[@Zhou], libpointmatcher [@Pomerleau] are commonly used in real-time applications
+owing to their performant implementations.
 Although they offer numerous functionalities, including those required for point cloud
 registration, they present several challenges for practical applications and scientific
 research.
@@ -106,6 +109,15 @@ distribution-to-distribution correspondence).
 
 More details can be found at https://github.com/koide3/small_gicp/blob/master/BENCHMARK.md.
 
+# Future work
+
+The efficiency of nearest neighbor search significantly impacts the overall performance of point cloud registration.
+While small_gicp currently offers efficient and parallel implementations of KdTree and voxelmap, which are general and useful in many situations,
+there are other nearest neighbor search methods that can be more efficient under mild assumptions about the point cloud measurement model
+(e.g., projective search [@Serafin]).
+We plan to implement these alternative neighbor search algorithms to further enhance the speed of the point cloud registration process.
+The design of small_gicp, where nearest neighbor search and pose optimization are decoupled, facilitates the easy integration of these new search algorithms.
+
 # Acknowledgements
 
 This work was supported in part by JSPS KAKENHI Grant Number 23K16979.