r""" Args: query, key, value: map a query and a set of key-value pairs to an output. See "Attention Is All You Need" for more

Args: xyz: (B,K,3) features: (B,C,K)

Input: xyz: input points position data, [B, N, C] points: input points data, [B, N, C] Return: ne

Input: xyz: input points position data, [B, C, N] points: input points data, [B, D, N] Return: ne

Input: xyz1: input points position data, [B, C, N] xyz2: sampled input points position data, [B, C, S] po

Forward pass. Arguments: seed_features: (batch_size, feature_dim, num_seed) Pytorch tensor Returns: logits: (

Args: xyz: (B,K,3) features: (B,C,K)

box_size: [x1,x2,...,xn,3] heading_angle: [x1,x2,...,xn] center: [x1,x2,...,xn,3] Return: [x1,x3,...,xn,8,3]

Compute IoU of two bounding boxes. ** Define your bod IoU function HERE **

Log a histogram of the tensor of values.

Args: labels: (B, N, 6) pred: (B, M, 6) iou_thresh: scalar conf_thresh: scalar label_mask: (B, N,) with v

Input is BxNx3 a batch of point cloud Output is BxIxIxnum_samplex3 Added on Feb 19

Input is BxNx3 batch of point cloud Output is Bx(vsize^3)

Input is BxNx3 a batch of point cloud Output is BxVxVxVxnum_samplex3 Added on Feb 19

vol is of size vsize*vsize*vsize output an image to output_filename

read XYZ for each vertex.

read XYZ RGB for each vertex. Note: RGB values are in 0-255

read XYZ point cloud from filename PLY file

Input ps is NxC points with first 3 channels as XYZ z is facing forward, x is left ward, y is downward

Input: (n,3), Output: (n,3)

Rotation about the y-axis.

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

Rotation about the z-axis.

Convert 3D box size (l,w,h) to size class and size residual

Accumulate one batch of prediction and groundtruth. Args: batch_pred_map_cls: a list of lists [[(pred_cls, pred_box_para

Visualize distance loss. Would be saved in `./statistics/s{scene_idx}-{time_str}/`

Visualize oriented bounding box ground truth pc: (N,3) label: (K,3) K == MAX_NUM_OBJ mask: (K,) angle_classes: (K,) angle_residu

Visualize point votes and point votes mask labels pc: (N,3 or 6), point_votes: (N,9), point_votes_mask: (N,)

Export scene bbox to meshes Args: scene_bbox: (N x 6 numpy array): xyz pos of center and 3 lengths out_filename: (string) filename

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

Export oriented (with normal vectors) scene bbox to meshes Args: center: Nx3, ndarray normal: Nx2, ndarray size: Nx2, nda

input: Nx3, write points to filename as PLY format.

Color (N,3) points with labels (N) within range 0 ~ num_classes-1 as OBJ file

Color (N,3) points with RGB colors (N,3) within range [0,255] as OBJ file