def get_center_regression_net(object_point_cloud, one_hot_vec, is_training,
bn_decay, end_points):
''' Regression network for center delta. a.k.a. T-Net.
Input:
object_point_cloud: TF tensor in shape (B,M,C)
point clouds in 3D mask coordinate
one_hot_vec: TF tensor in shape (B,3)
length-3 vectors indicating predicted object type
Output:
predicted_center: TF tensor in shape (B,3)
'''
num_point = object_point_cloud.get_shape()[1].value
net = tf.expand_dims(object_point_cloud, 2)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg1-stage1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg2-stage1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg3-stage1',
bn_decay=bn_decay)
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool-stage1')
net = tf.squeeze(net, axis=[1, 2])
net = tf.concat([net, one_hot_vec], axis=1)
net = tf_util.fully_connected(net,
256,
scope='fc1-stage1',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
net = tf_util.fully_connected(net,
128,
scope='fc2-stage1',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
predicted_center = tf_util.fully_connected(net,
3,
activation_fn=None,
scope='fc3-stage1')
return predicted_center, end_points
def get_3d_box_estimation_v1_net(self, object_point_cloud, one_hot_vec,
is_training, bn_decay, end_points):
''' 3D Box Estimation PointNet v1 network.
Input:
object_point_cloud: TF tensor in shape (B,M,C)
point clouds in object coordinate
one_hot_vec: TF tensor in shape (B,3)
length-3 vectors indicating predicted object type
Output:
output: TF tensor in shape (B,3+NUM_HEADING_BIN*2+NUM_SIZE_CLUSTER*4)
including box centers, heading bin class scores and residuals,
and size cluster scores and residuals
'''
num_point = object_point_cloud.get_shape()[1].value
net = tf.expand_dims(object_point_cloud, 2)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg2',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg3',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
512, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv-reg4',
bn_decay=bn_decay)
features = tf.reduce_max(net, axis=1)
net = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool2')
net = tf.squeeze(net, axis=[1, 2])
net = tf.concat([net, one_hot_vec], axis=1)
net = tf_util.fully_connected(net,
512,
scope='fc1',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
net = tf_util.fully_connected(net,
256,
scope='fc2',
bn=True,
is_training=is_training,
bn_decay=bn_decay)
# The first 3 numbers: box center coordinates (cx,cy,cz),
# the next NUM_HEADING_BIN*2: heading bin class scores and bin residuals
# next NUM_SIZE_CLUSTER*4: box cluster scores and residuals
output = tf_util.fully_connected(net,
3 + model_util.NUM_HEADING_BIN * 2 +
model_util.NUM_SIZE_CLUSTER * 4,
activation_fn=None,
scope='fc3')
return output, end_points, features
def get_instance_seg_v1_net(self, point_cloud, one_hot_vec, is_training,
bn_decay, end_points):
''' 3D instance segmentation PointNet v1 network.
Input:
point_cloud: TF tensor in shape (B,N,4)
frustum point clouds with XYZ and intensity in point channels
XYZs are in frustum coordinate
one_hot_vec: TF tensor in shape (B,3)
length-3 vectors indicating predicted object type
is_training: TF boolean scalar
bn_decay: TF float scalar
end_points: dict
Output:
logits: TF tensor in shape (B,N,2), scores for bkg/clutter and object
end_points: dict
'''
num_point = point_cloud.get_shape()[1].value
net = tf.expand_dims(point_cloud, 2)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv1',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv2',
bn_decay=bn_decay)
point_feat = tf_util.conv2d(net,
64, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv3',
bn_decay=bn_decay)
net = tf_util.conv2d(point_feat,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv4',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
1024, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv5',
bn_decay=bn_decay)
global_feat = tf_util.max_pool2d(net, [num_point, 1],
padding='VALID',
scope='maxpool')
global_feat = tf.concat(
[global_feat,
tf.expand_dims(tf.expand_dims(one_hot_vec, 1), 1)],
axis=3)
global_feat_expand = tf.tile(global_feat, [1, num_point, 1, 1])
concat_feat = tf.concat(axis=3,
values=[point_feat, global_feat_expand])
net = tf_util.conv2d(concat_feat,
512, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv6',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
256, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv7',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv8',
bn_decay=bn_decay)
net = tf_util.conv2d(net,
128, [1, 1],
padding='VALID',
stride=[1, 1],
bn=True,
is_training=is_training,
scope='conv9',
bn_decay=bn_decay)
net = tf_util.dropout(net, is_training, 'dp1', keep_prob=0.5)
logits = tf_util.conv2d(net,
2, [1, 1],
padding='VALID',
stride=[1, 1],
activation_fn=None,
scope='conv10')
logits = tf.squeeze(logits, [2]) # BxNxC
return logits, end_points