From 2409681f40de8e1f8d2f0771e99ee323655ab93a Mon Sep 17 00:00:00 2001 From: fabiopoiesi Date: Thu, 20 May 2021 09:12:26 +0200 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6942476..d21758a 100644 --- a/README.md +++ b/README.md @@ -12,6 +12,6 @@ Point cloud patches are extracted, canonicalised with respect to their local ref Our descriptors can effectively generalise across different sensor modalities from locally and randomly sampled points. The graphs above show the comparison between our descriptors and state-of-the-art descriptors on several indoor and outdoor datasets reconstructed using both RGBD sensors and laser scanners. In particular, [3DMatch](https://3dmatch.cs.princeton.edu/) is an indoor dataset captured with RGBD sensors, [ETH](https://projects.asl.ethz.ch/datasets/doku.php?id=laserregistration:laserregistration) and [KITTI](http://www.cvlibs.net/datasets/kitti/eval_odometry.php) are outdoor datasets captured with laser scanners. -Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and become the state of the art also in benchmarks where training and testing are performed in the same scenarios. +Our descriptors outperform most recent descriptors by a large margin in terms of generalisation, and become the state of the art also in benchmarks where training and testing are performed in same scenarios. [Paper (pdf)]()