Update README.md

README.md

@@ -13,4 +13,29 @@ on DCNN. In addition, DCNNs have great potential in processing the multi-
 sensory data, which usually contains rich information in the raw data and is
 sensitive to training time as well as model size. However, the multisensor fusion
 approaches suffer from two challenges, which are (1) the feature extraction from
-various types of sensory data and (2) the selection of a suitable fusion level.
+various types of sensory data and (2) the selection of a suitable fusion level. In this repository, we introduce the trend of DCNN-based multisensor fusion for object detection. We also describe some of our research objectives and contributions in this topic.
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+
+# Fusing LiDAR and Color Imagery for Object Detection using Convolutional Neural Networks [1]: 
+The goal of this work is answer to this question:
+how much fusing LiDAR and color images can improve the
+performance of a convolutional neural network (CNN)-based
+detector? To this end, we trained state-of-the-art CNN-based
+detectors using different configurations of color images and
+their associated LiDAR data, in conjunction and independently.
+Moreover, we investigate the effect of sparse and dense LiDAR
+data on the detection accuracy. For this purpose, we estimate
+a dense depth image from spare LiDAR data using a recent
+self-supervised depth completion technique [2] that requires only
+sequences of color and sparse depth images, without the need for
+dense depth labels. Then, we compared two detectors when are
+trained on sparse or dense LiDAR data. The obtained results
+on the KITTI dataset show that fusing dense LiDAR and color
+images is an efficient solution for future object detectors.
+
+# 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
+(Fusion), 2020.
+2. Fangchang Ma, Guilherme Venturelli Cavalheiro, and Sertac Karaman.
+Self-supervised sparse-to-dense: Self-supervised depth completion from
+lidar and monocular camera. CoRR, abs/1807.00275, 2018.
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