read calibration file with given filename Returns ------- dict Calibration matrices as 4x4 numpy arrays.

read poses file with per-scan poses from given filename Returns ------- list list of poses as 4x4 numpy arrays.

input is Nx3 points output is imgsize*imgsize*num_sample*3 assumes points are in range [-radius, radius] samples num_sample p

input is Nx3 points. output is vsize*vsize*vsize assumes points are in range [-radius, radius]

input is Nx3 points output is vsize*vsize*vsize*num_sample*3 assumes points are in range [-radius, radius] samples num_sample

process_file. Args: filepath: path to the main file mode: typically train, test or validation Returns:

process_file. Please note, that for obtaining segmentation labels json files were used. Args: filepath: path to the main

points is a Nx3 numpy array

Rotation about the y-axis.

Rotation about the y-axis. t: (x1,x2,...xn) return: (x1,x2,...,xn,3,3)

Save an RGB point cloud as a PLY file. Args: points_3d: Nx6 matrix where points_3d[:, :3] are the XYZ coordinates and points_3d[:, 4:] are

Log a scalar variable.

Test point cloud data loader.

ap = voc_ap(rec, prec, [use_07_metric]) Compute VOC AP given precision and recall. If use_07_metric is true, uses the VOC 07 11 point met

vol is occupancy grid (value = 0 or 1) of size vsize*vsize*vsize return Nx3 numpy array.

zca whiten each feature map within individual image in batch

Transform from world coordinates (3D) to camera coordinates (3D).

Create lines represented as cylinders connecting pairs of 3D points Args: pcl: (N x 2 x 3 numpy array): N pairs of xyz pos

Export oriented (around Z axis) scene bbox to meshes Args: scene_bbox: (N x 7 numpy array): xyz pos of center and 3 lengths (dx,dy,dz)

Do this only in debug mode...