Construct a tree over data points. Params: pa : data points no_dims: number of dimensions n : number of data points bsp : number of p

Delete tree Params: tree : Tree struct of kd tree ************************************************/

Delete tree Params: tree : Tree struct of kd tree ************************************************/

Computes minimal distances of each point in points_src to points_tgt. Args: points_src (numpy array): source points normals_src (

Insert point into priority queue Params: closest_idx : index queue closest_dist : distance queue pidx : permutation index of data points

Insert point into priority queue Params: closest_idx : index queue closest_dist : distance queue pidx : permutation index of data points

:param c_m: n_parts, latent_size :param c_s: n_parts, latent_size :param pts: 1, n_optim_points, 3 :param rot_mtx: 1,

Search for nearest neighbour for a set of query points Params: tree : Tree struct of kd tree pa : data points pidx : permutation index of

Search for nearest neighbour for a set of query points Params: tree : Tree struct of kd tree pa : data points pidx : permutation index of

Write point cloud to ply file. Args: filename: str, filename for ply file to load. v: np.array of shape [#v, 3], vertex coordinates

BufferFormatCheck */

KeywordStringCheck */

Import */

InitStrings */

CIntToPy */

RaiseDoubleKeywords */

RaiseNeedMoreValuesToUnpack */

RaiseNoneIterError */

RaiseTooManyValuesToUnpack */

CheckBinaryVersion */