def create_loss(output, truth):
# return tf.reduce_mean(tf.square(output-truth))
match = approx_match(output, truth)
repulsion_loss = get_repulsion_loss(output)
emd_loss = tf.reduce_mean(match_cost(output, truth, match))
cost_p1_p2, _, cost_p2_p1, _ = nn_distance(output, truth)
chamfer_loss = tf.reduce_mean(
tf.reduce_sum(cost_p1_p2, -1) + tf.reduce_sum(cost_p2_p1, -1))
return emd_loss, chamfer_loss, repulsion_loss
def create_loss_local(output, truth, loss_type='emd'):
# return tf.reduce_mean(tf.square(output-truth))
output = tf.reshape(output,
[-1, output.get_shape()[2],
output.get_shape()[3]])
truth = tf.reshape(truth, [-1, truth.get_shape()[2], truth.get_shape()[3]])
if loss_type == 'emd':
match = approx_match(output, truth)
return tf.reduce_mean(match_cost(output, truth, match))
else:
cost_p1_p2, _, cost_p2_p1, _ = nn_distance(output, truth)
return tf.reduce_mean(cost_p1_p2) + tf.reduce_mean(cost_p2_p1)
def get_chamfer_loss_pcn(output, truth):
cost_p1_p2, _, cost_p2_p1, _ = nn_distance(output, truth)
cost_p1_p2 = tf.reduce_mean(tf.sqrt(cost_p1_p2))
cost_p2_p1 = tf.reduce_mean(tf.sqrt(cost_p2_p1))
return (cost_p1_p2 + cost_p2_p1) / 2
def get_eval_chamfer_loss(output, truth):
cost_p1_p2, _, cost_p2_p1, _ = nn_distance(output, truth)
chamfer_loss = tf.reduce_sum(cost_p1_p2, -1) + tf.reduce_sum(
cost_p2_p1, -1)
return chamfer_loss
def chamfer_single_side(pcd1, pcd2):
dist1, _, dist2, _ = tf_nndistance.nn_distance(pcd1, pcd2)
mdist1 = tf.reduce_mean(dist1)
return mdist1
def chamfer(pcd1, pcd2):
dist1, _, dist2, _ = tf_nndistance.nn_distance(pcd1, pcd2)
mdist1 = tf.reduce_mean(dist1)
mdist2 = tf.reduce_mean(dist2)
return mdist1 + mdist2