example: Add an example of Pointnet inference implementation

examples/network/README.md

@@ -1,10 +1,12 @@
 # PointNet Convolutional Neural Network Sample for 3D Pointcloud Classification
 
-[PointNet][pointnet-paper] is a convolutional neural network architecture for applications concerning 3D recognition such as object classification and part segmentation. These sample codes implement a variant of PointNet for 3D object classification, for inference only with ModelNet10, showing a larger example of using oneDNN. Some rough instructions for how it might be used are provided.
+[PointNet][pointnet-paper] is a convolutional neural network architecture for applications concerning 3D recognition such as object classification and part segmentation. These sample codes implement a variant of PointNet for 3D object classification, for inference only with ModelNet10, providing a comprehensive example of using oneDNN. You can see the following initial instructions on using the samples.
 
-## Obtaining the model weights and classes and preparing an input pointcloud
+## Obtain the Model Weights and Classes and Preparing an Input pointcloud
 
-A preprocessing script is provided which unpacks the weights from a pretrained pytorch model. The script also prepares an input pointcloud for testing inference. The pointcloud is made from 3D scans taken from the [ModelNet10][modelnet] dataset. The script requires an installation of [PyTorch][pytorch]. First download the pretrained PointNet weights and move the pth file into the same directory of the model.
+A preprocessing script is provided which unpacks the weights from a pre-trained pytorch model. The script also prepares an input pointcloud for testing inference. The pointcloud is made from 3D scans taken from the [ModelNet10][modelnet] dataset. The script requires an installation of [PyTorch][pytorch].
+
+First, download the pre-trained PointNet weights and then move the pth file into the same directory containing the model.
 
 ```bash
 wget http://3dvision.princeton.edu/projects/2014/3DShapeNets/ModelNet10.zip
@@ -13,16 +15,17 @@ rm ModelNet10.zip
 python3 prepareData.py ModelNet10/ pointnet_model.pth
 ```
 
-The weights will be saved to `data/` and the input pointcloud will be saved as `itemName_cloud.bin`
+The weights will be saved to `data/` and the input pointcloud will be saved as `itemName_cloud.bin`.
 
-## Testing on a pointcloud
+## Test on a pointcloud
 
 The oneDNN samples are built in the default CMake configuration. The sample
-is built by the target `network-pointnet-cpp`. The samples must first
-be passed the directory where the binary weights files are stored and the second
-argument should be the preprocessed pointcloud that should be classified. The expected
-output is of a classification index and a series of times in nanoseconds that corresond
-to the total time to run the network on an input, not including data transfer time.
+is built by the target `network-pointnet-cpp`.
+The oneDNN samples are built using the default CMake configuration.
+To run the sample, provide as first argument the path to the directory containing the binary weight files and as second argument the path to the preprocessed point cloud file to be classified.
+
+The expected output includes a classification index and (if enabled) timing measurements in nanoseconds, representing the total time taken to run the network on the input, excluding data transfer time.
+
 
 ```bash
 network-pointnet-cpp ModelNet10/directory/extracted_data ModelNet10/directory/input_cloud/itemName_cloud.bin