Computes the precision@k for the specified values of k

Prepare block training data. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is s

Prepare block training data. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is s

room2block with input filename and RGB preprocessing.

room2block with input filename and RGB preprocessing.

room2block, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

room2block, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

Prepare whole room samples. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is s

Prepare whole room samples. Args: data: N x 6 numpy array, 012 are XYZ in meters, 345 are RGB in [0,1] assumes the data is s

room2sample, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

room2sample, with input filename and RGB preprocessing. for each block centralize XYZ, add normalized XYZ as 678 channels

data is in N x ... we want to keep num_samplexC of them. if N > num_sample, we will randomly keep num_sample of them. if N <

data is in N x ... we want to keep num_samplexC of them. if N > num_sample, we will randomly keep num_sample of them. if N <

Shuffle data and labels. Input: data: B,N,... numpy array label: B,... numpy array Return: shuffled dat

Sets the learning rate to the initial LR decayed by 10 every 30 epochs

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filenam

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filenam

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filenam

Visualization of bounding boxes. Args: input_filename: each line is x1 y1 z1 x2 y2 z2 label out_filename_prefix: OBJ filenam

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Compute bounding boxes from each instance in original dataset files on one room. **We assume the bbox is aligned with XYZ coordinate.**

Convert original dataset files to data_label file (each line is XYZRGBL). We aggregated all the points from each instance in the room. A

Convert original dataset files to data_label file (each line is XYZRGBL). We aggregated all the points from each instance in the room. A

For visualization of a room from data_label file, input_filename: each line is X Y Z R G B L out_filename: OBJ filename, visualize inpu

For visualization of a room from data_label file, input_filename: each line is X Y Z R G B L out_filename: OBJ filename, visualize inpu