From c898bf8e9f018e6f882df2ed2176f6cf2c7e4353 Mon Sep 17 00:00:00 2001 From: Fahimeh Farahnakian <fahimeh.farahnakian@utu.fi> Date: Wed, 15 Jul 2020 13:36:14 +0300 Subject: [PATCH] Update README.md --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 24e5d12..93d1985 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,12 @@ Fig.1(c). The dense image is obtained through self-supervised algorithm [1]. Thi the two above frameworks as there is not fusion in this experiment as well. 4) Color and dense LiDAR-based framework: uses both -color and dense LiDAR images for training the detec-tion network as shown in Fig.1(d). This framework is -described in Section III +color and dense LiDAR images for training the detec-tion network as shown in Fig.1(d). + + + +Fig. 1. The proposed (a) Color-based (b) Sparse LiDAR-based (c) Dense LiDAR-based and (d) Color and dense LiDAR based frameworks. # References 1. F. Farahnakian, and J. Heikkonen, “Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks”, The 23th edition of the IEEE International conference on information fusion -- GitLab