def batch_neighbors(queries, supports, q_batches, s_batches, radius):
"""
Computes neighbors for a batch of queries and supports
:param queries: (N1, 3) the query points
:param supports: (N2, 3) the support points
:param q_batches: (B) the list of lengths of batch elements in queries
:param s_batches: (B)the list of lengths of batch elements in supports
:param radius: float32
:return: neighbors indices
"""
return cpp_neighbors.batch_query(queries, supports, q_batches, s_batches, radius=radius)
def batch_neighbors(queries, supports, q_batches, s_batches, radius):
"""
调用cpp_wrapper里面的cpp_neighbors
可以看出queries 和 supports 的batches的个数是一样的
return 值的意义 详见neighbors。按顺序储存，储存的是对于当前的queries的点，它在自己所属的batches中，半径为r的范围的点索引
如果第一个参数和第二个参数一致，功能就变成了寻找当前batches中，当前点到其他点小于radius的点的索引
Computes neighbors for a batch of queries and supports
:param queries: (N1, 3) the query points
:param supports: (N2, 3) the support points
:param q_batches: (B) the list of lengths of batch elements in queries
:param s_batches: (B) the list of lengths of batch elements in supports
:param radius: float32
:return: neighbors indices
"""
return cpp_neighbors.batch_query(queries, supports, q_batches, s_batches, radius=radius)
def batch_neighbors_kpconv(queries, supports, q_batches, s_batches, radius,
max_neighbors):
"""
Computes neighbors for a batch of queries and supports, apply radius search
:param queries: (N1, 3) the query points
:param supports: (N2, 3) the support points
:param q_batches: (B) the list of lengths of batch elements in queries
:param s_batches: (B)the list of lengths of batch elements in supports
:param radius: float32
:return: neighbors indices
"""
neighbors = cpp_neighbors.batch_query(queries,
supports,
q_batches,
s_batches,
radius=radius)
if max_neighbors > 0:
return torch.from_numpy(neighbors[:, :max_neighbors])
else:
return torch.from_numpy(neighbors)
def batch_neighbors(queries, supports, q_batches, s_batches, radius):
return cpp_neighbors.batch_query(queries,
supports,
q_batches,
s_batches,
radius=radius)