3D_CNN_tensorflow
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yukitsuji
KITTI data processing and 3D CNN for Vehicle Detection
I tired to execute the this funtion `def create_labels(places, size, corners, resolution = 0.5, x=(0, 90), y=(-50, 50), z=(-4.5, 5.5), scale=4, min_value=np.array([0., -50., -4.5])):` but the returned variables `sphere_center, train_corners`...
Hello, can anyone explain what is the utility of this function `create_objectness_label` and what does it do in the train process. Also, in the `train_test()` function in `model_01_deconv` I couldn't...
Can anyone tell me how the bounding boxes are predcition with the model? and which function does that? Thank you.
Can anyone tell me how can I voxelize a batch of frames at once instead of voxelizing a frame at a time? thank you
Can anyone explain what the deconvolutional layer `def deconv3D_to_output(input_layer, input_dim, output_dim, height, width, length, stride, output_shape, activation=tf.nn.relu, padding="SAME", name="")` is for? and does is influence the training/learning process? Thank you.
Since my resources are limited, can I use a pre trained model to detect vehicles with this voxelization method?
My main goal is to do a joint detection and tracking, and to achieve this i need to integrate the temporal information in the voxel representation to do the tracking....
I tried implementing the training function of the model_01_deconv.py function but I get errors starting with the first being the following: File "model_01_deconv.py", line 382, in lidar_generator yield np.array(batch_voxel, dtype=np.float32)[:,...
Hello, I have hard time to understand why the bounding boxes aren't 3D in my reproduction of the code. Any idea? thank you 
Hello, Im trying to re implement the volxelization process as I need it later on. But I'm having hard time understanding some function as I'm new to Python. I hope...