# ---------------------------------------------------------------------------- # - Open3D: www.open3d.org - # ---------------------------------------------------------------------------- # Copyright (c) 2018-2024 www.open3d.org # SPDX-License-Identifier: MIT # ---------------------------------------------------------------------------- from ...python.ops import ops import tensorflow as tf __all__ = ['FixedRadiusSearch', 'RadiusSearch', 'KNNSearch'] class FixedRadiusSearch(tf.keras.layers.Layer): """Fixed radius search for 3D point clouds. This layer computes the neighbors for a fixed radius on a point cloud. For batch support, you can either pass 'points' and 'queries' as RaggedTensor, or pass the 'row_splits' information. Example: This example shows a neighbor search that returns the indices to the found neighbors and the distances.:: import tensorflow as tf import open3d.ml.tf as ml3d points = tf.random.normal([20,3]) queries = tf.random.normal([10,3]) radius = 0.8 nsearch = ml3d.layers.FixedRadiusSearch(return_distances=True) ans = nsearch(points, queries, radius) # returns a tuple of neighbors_index, neighbors_row_splits, and neighbors_distance Arguments: metric: Either L1, L2 or Linf. Default is L2. ignore_query_point: If True the points that coincide with the center of the search window will be ignored. This excludes the query point if 'queries' and 'points' are the same point cloud. return_distances: If True the distances for each neighbor will be returned. If False a zero length Tensor will be returned instead. """ def __init__(self, metric='L2', ignore_query_point=False, return_distances=False, max_hash_table_size=32 * 2**20, index_dtype=tf.int32, **kwargs): self.metric = metric self.ignore_query_point = ignore_query_point self.return_distances = return_distances self.max_hash_table_size = max_hash_table_size self.index_dtype = index_dtype super().__init__(autocast=False, **kwargs) def build(self, inp_shape): super().build(inp_shape) def call(self, points, queries, radius, points_row_splits=None, queries_row_splits=None, hash_table_size_factor=1 / 64, hash_table=None): """This function computes the neighbors within a fixed radius for each query point. Arguments: points: The 3D positions of the input points. It can be a RaggedTensor. *This argument must be given as a positional argument!* queries: The 3D positions of the query points. It can be a RaggedTensor. radius: A scalar with the neighborhood radius points_row_splits: Optional 1D vector with the row splits information if points is batched. This vector is [0, num_points] if there is only 1 batch item. queries_row_splits: Optional 1D vector with the row splits information if queries is batched. This vector is [0, num_queries] if there is only 1 batch item. hash_table_size_factor: Scalar. The size of the hash table as fraction of points. hash_table: A precomputed hash table generated with build_spatial_hash_table(). This input can be used to explicitly force the reuse of a hash table in special cases and is usually not needed. Note that the hash table must have been generated with the same 'points' array. Returns: 3 Tensors in the following order neighbors_index The compact list of indices of the neighbors. The corresponding query point can be inferred from the 'neighbor_count_row_splits' vector. neighbors_row_splits The exclusive prefix sum of the neighbor count for the query points including the total neighbor count as the last element. The size of this array is the number of queries + 1. neighbors_distance Stores the distance to each neighbor if 'return_distances' is True. Note that the distances are squared if metric is L2. This is a zero length Tensor if 'return_distances' is False. """ if isinstance(points, tf.RaggedTensor): points_row_splits = points.row_splits points = points.values if isinstance(queries, tf.RaggedTensor): queries_row_splits = queries.row_splits queries = queries.values if isinstance(radius, tf.Tensor): radius_ = tf.cast(radius, points.dtype) else: radius_ = radius if points_row_splits is None: points_row_splits = tf.cast(tf.stack([0, tf.shape(points)[0]]), dtype=tf.int64) if queries_row_splits is None: queries_row_splits = tf.cast(tf.stack([0, tf.shape(queries)[0]]), dtype=tf.int64) if hash_table is None: table = ops.build_spatial_hash_table( max_hash_table_size=self.max_hash_table_size, points=points, radius=radius_, points_row_splits=points_row_splits, hash_table_size_factor=hash_table_size_factor) else: table = hash_table result = ops.fixed_radius_search( ignore_query_point=self.ignore_query_point, return_distances=self.return_distances, metric=self.metric, points=points, queries=queries, radius=radius_, points_row_splits=points_row_splits, queries_row_splits=queries_row_splits, hash_table_splits=table.hash_table_splits, hash_table_index=table.hash_table_index, hash_table_cell_splits=table.hash_table_cell_splits, index_dtype=self.index_dtype) return result class RadiusSearch(tf.keras.layers.Layer): """Radius search for 3D point clouds. This layer computes the neighbors for each query point with each query having an individual radius. Example: This example shows a neighbor search that returns the indices to the found neighbors and the distances.:: import tensorflow as tf import open3d.ml.tf as ml3d points = tf.random.normal([20,3]) queries = tf.random.normal([10,3]) radii = tf.random.normal([10], mean=1.0) nsearch = ml3d.layers.RadiusSearch(return_distances=True) ans = nsearch(points, queries, radii) # returns a tuple of neighbors_index, neighbors_row_splits, and neighbors_distance Arguments: metric: Either L1, L2 or Linf. Default is L2. ignore_query_point: If True the points that coincide with the center of the search window will be ignored. This excludes the query point if 'queries' and 'points' are the same point cloud. return_distances: If True the distances for each neighbor will be returned. If False a zero length Tensor will be returned instead. normalize_distances: If True the returned distances will be normalized with the radii. """ def __init__(self, metric='L2', ignore_query_point=False, return_distances=False, normalize_distances=False, index_dtype=tf.int32, **kwargs): self.metric = metric self.ignore_query_point = ignore_query_point self.return_distances = return_distances self.normalize_distances = normalize_distances self.index_dtype = index_dtype super().__init__(autocast=False, **kwargs) def build(self, inp_shape): super().build(inp_shape) def call(self, points, queries, radii, points_row_splits=None, queries_row_splits=None): """This function computes the neighbors within a radius for each query point. Arguments: points: The 3D positions of the input points. *This argument must be given as a positional argument!* queries: The 3D positions of the query points. radii: A radius for each query point. points_row_splits: Optional 1D vector with the row splits information if points is batched. This vector is [0, num_points] if there is only 1 batch item. queries_row_splits: Optional 1D vector with the row splits information if queries is batched. This vector is [0, num_queries] if there is only 1 batch item. Returns: 3 Tensors in the following order neighbors_index The compact list of indices of the neighbors. The corresponding query point can be inferred from the 'neighbor_count_row_splits' vector. neighbors_row_splits The exclusive prefix sum of the neighbor count for the query points including the total neighbor count as the last element. The size of this array is the number of queries + 1. neighbors_distance Stores the distance to each neighbor if 'return_distances' is True. Note that the distances are squared if metric is L2. This is a zero length Tensor if 'return_distances' is False. """ if points_row_splits is None: points_row_splits = tf.cast(tf.stack([0, tf.shape(points)[0]]), dtype=tf.int64) if queries_row_splits is None: queries_row_splits = tf.cast(tf.stack([0, tf.shape(queries)[0]]), dtype=tf.int64) result = ops.radius_search(ignore_query_point=self.ignore_query_point, return_distances=self.return_distances, normalize_distances=self.normalize_distances, metric=self.metric, points=points, queries=queries, radii=radii, points_row_splits=points_row_splits, queries_row_splits=queries_row_splits, index_dtype=self.index_dtype) return result class KNNSearch(tf.keras.layers.Layer): """KNN search for 3D point clouds. This layer computes the k nearest neighbors for each query point. Example: This example shows a neighbor search that returns the indices to the found neighbors and the distances.:: import tensorflow as tf import open3d.ml.tf as ml3d points = tf.random.normal([20,3]) queries = tf.random.normal([10,3]) k = 8 nsearch = ml3d.layers.KNNSearch(return_distances=True) ans = nsearch(points, queries, k) # returns a tuple of neighbors_index, neighbors_row_splits, and neighbors_distance # Since there are more than k points and we do not ignore any points we can # reshape the output to [num_queries, k] with neighbors_index = tf.reshape(ans.neighbors_index, [10,k]) neighbors_distance = tf.reshape(ans.neighbors_distance, [10,k]) Arguments: metric: Either L1, L2 or Linf. Default is L2. ignore_query_point: If True the points that coincide with the center of the search window will be ignored. This excludes the query point if 'queries' and 'points' are the same point cloud. return_distances: If True the distances for each neighbor will be returned. If False a zero length Tensor will be returned instead. """ def __init__(self, metric='L2', ignore_query_point=False, return_distances=False, index_dtype=tf.int32, **kwargs): self.metric = metric self.ignore_query_point = ignore_query_point self.return_distances = return_distances self.index_dtype = index_dtype super().__init__(autocast=False, **kwargs) def build(self, inp_shape): super().build(inp_shape) def call(self, points, queries, k, points_row_splits=None, queries_row_splits=None): """This function computes the k nearest neighbors for each query point. Arguments: points: The 3D positions of the input points. *This argument must be given as a positional argument!* queries: The 3D positions of the query points. k: The number of nearest neighbors to search. points_row_splits: Optional 1D vector with the row splits information if points is batched. This vector is [0, num_points] if there is only 1 batch item. queries_row_splits: Optional 1D vector with the row splits information if queries is batched. This vector is [0, num_queries] if there is only 1 batch item. Returns: 3 Tensors in the following order neighbors_index The compact list of indices of the neighbors. The corresponding query point can be inferred from the 'neighbor_count_row_splits' vector. neighbors_row_splits The exclusive prefix sum of the neighbor count for the query points including the total neighbor count as the last element. The size of this array is the number of queries + 1. neighbors_distance Stores the distance to each neighbor if 'return_distances' is True. Note that the distances are squared if metric is L2. This is a zero length Tensor if 'return_distances' is False. """ if points_row_splits is None: points_row_splits = tf.cast(tf.stack([0, tf.shape(points)[0]]), dtype=tf.int64) if queries_row_splits is None: queries_row_splits = tf.cast(tf.stack([0, tf.shape(queries)[0]]), dtype=tf.int64) result = ops.knn_search(ignore_query_point=self.ignore_query_point, return_distances=self.return_distances, metric=self.metric, points=points, queries=queries, k=k, points_row_splits=points_row_splits, queries_row_splits=queries_row_splits, index_dtype=self.index_dtype) return result