Update README.md

README.md

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-# 3D Object Detection
+# 3D Object Detection
+
+# Product Description
+
+This product identifies and labels 3D Objects in images of every day settings, such as cars, trees, bikes, pedestrians, etc. 
+
+This product makes use of a UNet, which is a Convolutional Neural Network, to identify objects, given voxel data. Our product first takes point cloud data from the SemanticKITTI dataset, and converts it to voxels. For the sake of simplicity, a voxel can be described as a 3d pixel. We visualize these voxels as cubes, each cube containing spatial information in 3 dimensions.
+
+
+# Directory Guide
+
+data_generator.py
+  - loads file paths of data into memory, when you call __getItem__(index) it reads the binary point cloud data and converts it to a voxel grid, and outputs the labels 
+
+index_data.py
+  - process the file paths for the data generator
+
+Velodyne.py
+  - converts the binary lidar file to a list of 3D points
+
+Labels.py
+  - Reads labels from the .label files
+
+Citations
+J. Behley, A. Milioto, C. Stachniss, M. Garbade, J. Gall, J. Quenzel, and S. Behnke, “Semantic Kitti Dataset Overview,” Semantickitti - A dataset for LIDAR-based Semantic Scene Understanding, 2020. [Online]. Available: http://www.semantic-kitti.org/dataset.html. [Accessed: 08-Dec-2021].