Perform RANSAC match on given point cloud pair Parameters ---------- pcd_source: open3d.geometry.PointCloud source point clo

Perform RANSAC match on given point cloud pair Parameters ---------- pcd_source: open3d.geometry.PointCloud source point clo

Read KITTI 3D Object calibration data for frame transformations Parameters ---------- filepath: str File path of KITTI 3D Ob

Read KITTI 3D Object label as Pandas DataFrame Parameters ---------- filepath: str File path of KITTI 3D Object label R

Read ModelNet40 sample with normal as Open3D point cloud Parameters ---------- filepath: str File path of ModelNet40 sample

Read ModelNet40 sample with normal as Open3D point cloud Parameters ---------- filepath: str File path of ModelNet40 sample

:param path: :return: homography matrix of the point cloud, N*3

Read Velodyne measurements from input bin file Parameters ---------- filepath: str File path of Velodyne measurements as bin

Receive and decode WebSocket frames. Returns: (bufs_list, closed_string)

Run the given function in an independent python interpreter.

Get segmented ground plane

Segment ground plane and foreground objects from Velodyne measurement Parameters ---------- point_cloud: numpy.ndarray Velod

Returns which sub-protocol should be used. This method does not select any sub-protocol by default and is meant to be overridden by a

Send a WebSocket orderly close frame.

Encode and send WebSocket frames. Any frames already queued will be sent first. If buf is not set then only queued frames will be sen

Send a WebSocket ping frame.

Serialize Parameters ---------- xyz: numpy.ndarray Point cloud coordinates. points: numpy.nda

Serialize Parameters ---------- xyz: numpy.ndarray Point cloud coordinates. points: numpy.nda

Called after WebSockets startup

Called automatically after each test.

input: points (b,m,c), idx (b,n,3), weight (b,n,3) output: out (b,n,c)

input: points (b,m,c), idx (b,n,3), weight (b,n,3) output: out (b,n,c)

input: grad_out (b,n,c), idx (b,n,3), weight (b,n,3) output: grad_points (b,m,c)

input: grad_out (b,n,c), idx (b,n,3), weight (b,n,3) output: grad_points (b,m,c)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

Find three nearest neigbors with square distance input: xyz1 (b,n,3), xyz2(b,m,3) output: dist (b,n,3), idx (b,n,3)

Do something with a WebSockets client connection.

WebSocketServer logging/output functions

Transform points from camera frame into velodyne frame Parameters ---------- X_cam: numpy.ndarray Points in camera frame

Transform points from velodyne frame into object frame Parameters ---------- X_velo: numpy.ndarray Points in velodyne frame

Transform point from Velodyne frame to camera frame Parameters ---------- X_velo: numpy.ndarray points in velodyne frame

Transform point from camera frame to pixel frame Parameters ---------- X_cam: numpy.ndarray points in camera frame param

Ensure that the connection is a valid connection, and set the target.

Downsample point cloud using voxel grid Parameters: points(pandas.DataFrame): points in the point cloud leaf_size(float): voxel

<summary> Clean up any resources being used. </summary> <param name="disposing">true if managed resources should be disposed; otherwise, false.</param

<summary> Clean up any resources being used. </summary> <param name="disposing">true if managed resources should be disposed; otherwise, false.</param