From d45d5ac5928b26779f161b2d584217c63bc3f217 Mon Sep 17 00:00:00 2001
From: FPrasetiawan <89938578+FPrasetiawan@users.noreply.github.com>
Date: Wed, 8 Dec 2021 17:52:52 -0500
Subject: [PATCH 1/3] Update README.md

---
 README.md | 22 +++++++++++++++++++++-
 1 file changed, 21 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index fcf1b52..9655324 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,21 @@
-# 3D Object Detection
\ No newline at end of file
+# 3D Object Detection
+
+
+
+
+# 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
+

From 5370266f95060010a545d8b66ce667ed64612e57 Mon Sep 17 00:00:00 2001
From: Jordan Chen <57933842+jordsch@users.noreply.github.com>
Date: Thu, 9 Dec 2021 21:05:45 -0500
Subject: [PATCH 2/3] Update README.md

added citations
---
 README.md | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/README.md b/README.md
index 9655324..6fd6123 100644
--- a/README.md
+++ b/README.md
@@ -19,3 +19,5 @@ Velodyne.py
 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].

From 688543b2d24ebcc40dbd44b89d1d55cb88e9281a Mon Sep 17 00:00:00 2001
From: atharvtrasi <45699336+atharvtrasi@users.noreply.github.com>
Date: Fri, 10 Dec 2021 01:16:31 -0500
Subject: [PATCH 3/3] Update README.md

---
 README.md | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 6fd6123..284d1da 100644
--- a/README.md
+++ b/README.md
@@ -1,11 +1,13 @@
 # 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. 
 
-
-# Directory Guide
+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