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pre_min_cube_size: Voxel size at the lowest resultion. It is doubled at each downsampling stage. -
pre_filter_size: Number of elements in each dimension of the convolutional kernel. -
pre_num_scales: The number of resolutions to use. -
pre_num_rotations: Number of rotations used for rotational augmentation. -
pre_num_neighbors: Number of neighboring points used to precompute the structure of conv kernel. -
pre_noise_level: Standard deviation of the additive Gaussian noise on point locations. -
pre_output_dir: Directory for storing data after pre-computation. -
pre_interp_method: Method for interpolating the signal in tangent images ('depth_densify_nearest_neighbor', 'depth_densify_gaussian_kernel'). -
pre_dataset_param: Dataset type ('stanford', 'scannet' or 'semantic3d').
co_train_file: List of training scans.co_test_file: List of test scans.co_experiment_dir: Path to current experiment directory.co_output_dir: Relative path to store network outputs.
tt_log_dir: Directory where to output logs.tt_snapshot_dir: Directory for saving network snapshots.tt_input_type: Which input features to use for training. A string containing one or more of the following values:c(color),d(depth),n(normals),h(height).tt_max_snapshots: Maximum number of snapshots to be saved.tt_test_iter: Frequency of running the validation during training (in iterations).tt_reload_iter: Frequrency of updating the training set (in iterations). Used because of the rotational augmentation, to not store all scans in memory all the time.tt_max_iter_count: Maximum number of training iterations.tt_batch_size: Batch size.tt_valid_rad: Radius of a sphere to be used for sampling when working with large scans.tt_filter_size: Size of the convolutional filter along one dimension.tt_batch_array_size: Number of batches to pre-load when sampling from large scans.
eval_scan_file: Name pattern for the raw scan file.eval_label_file: Name pattern for the raw label file.eval_output_file: Name pattern for file with extrapolated labels.