pointpillars

.gitignore

@@ -8,3 +8,9 @@ cmake_example.egg-info/
 dist/
 logs/
 point_pillars.egg-info/
+logs_*/
+.vscode/
+.ipynb_checkpoints/
+archive/
+visualization/
+logs.zip

CMakeLists.txt

@@ -1,4 +1,5 @@
 cmake_minimum_required(VERSION 3.5)
 project(point_pillars)
 add_subdirectory(pybind11)
-pybind11_add_module(point_pillars SHARED src/point_pillars.cpp)
+pybind11_add_module(point_pillars SHARED src/point_pillars.cpp)
+pybind11_add_module(point_pillars_v2 SHARED src/point_pillars_v2.cpp)

README.md

@@ -1,3 +1,10 @@
+# Forked from (https://github.com/tyagi-iiitv/PointPillars.git) [Under Development]
+
+The files with the v2_2 extentions are scripts that does not do class regression.
+It only learn to classify single class. The confidence of objectness is used to determine 
+whether there is an object of a particular class or not.
+
+
 # About Point Pillars
 Point Pillars is a very famous Deep Neural Network for 3D Object Detection for LiDAR point clouds. With the application of object detection on the LiDAR devices fitted in the self driving cars, Point Pillars focuse on fast inference ~50fps, which was magnitudes above as compared to other networks for 3D Object detection. In this repo, we are trying to develop point pillars in TensorFlow. [Here's](https://medium.com/@a_tyagi/pointpillars-3d-point-clouds-bounding-box-detection-and-tracking-pointnet-pointnet-lasernet-67e26116de5a?source=friends_link&sk=4a27f55f2cea645af39f72117984fd22) a good first post to familiarize yourself with Point Pillars.