def pts_in_boxes3d_cpu(pts, boxes3d):
"""
:param pts: (N, 3) in rect-camera coords
:param boxes3d: (M, 7)
:return: boxes_pts_mask_list: (M), list with [(N), (N), ..]
both inputs are pytorch tensors
choose the points inside a bounding box and label them as a single instance
"""
# if the pts tensor is not on GPU (it is on cpu)
if not pts.is_cuda:
pts = pts.float().contiguous()
boxes3d = boxes3d.float().contiguous()
# a table of which point is inside which box
pts_flag = torch.LongTensor(torch.Size(
(boxes3d.size(0), pts.size(0)))) # (M, N)
# 'pts_in_boxes3d_cpu' is a cpp function that is in the src folder (it is imported using the code in setup.py)
# it probably just fills the table 'pts_flag' (haven't ead it)
roipool3d_cuda.pts_in_boxes3d_cpu(pts_flag, pts, boxes3d)
boxes_pts_mask_list = []
for k in range(0, boxes3d.shape[0]):
cur_mask = pts_flag[k] > 0
boxes_pts_mask_list.append(cur_mask)
return boxes_pts_mask_list
else:
raise NotImplementedError
def pts_in_boxes3d_cpu(pts, boxes3d):
"""
:param pts: (N, 3) in rect-camera coords
:param boxes3d: (M, 7)
:return: boxes_pts_mask_list: (M), list with [(N), (N), ..]
"""
if not pts.is_cuda:
pts = pts.float().contiguous()
boxes3d = boxes3d.float().contiguous()
pts_flag = torch.LongTensor(torch.Size((boxes3d.size(0), pts.size(0)))) # (M, N)
roipool3d_cuda.pts_in_boxes3d_cpu(pts_flag, pts, boxes3d)
boxes_pts_mask_list = []
for k in range(0, boxes3d.shape[0]):
cur_mask = pts_flag[k] > 0
boxes_pts_mask_list.append(cur_mask)
return boxes_pts_mask_list
else:
raise NotImplementedError
