Really Low Quality Bundle Adjustment in Tensorflow

tf_bundle.py

guess = np.concatenate([np.array(map(lambda x: np.array(x[:,3]).ravel(), rts)), fpts[:, 0:3]], axis=0)
selects = []
xys  = []
for i, trk in enumerate(good_good_tracks):
  for shot, kp in trk:
    xy = G.node[(shot,kp)]['kp']/0.81412059
    xys.append(xy)
    select = np.zeros((len(frms)+len(good_good_tracks),))
    select[shot] = 1
    select[len(frms)+i] = 1
    selects.append(select)
selects, xys = np.array(selects), np.array(xys)

# initial loss
vt = np.dot(selects, guess)
proj = (vt[:, 0:2].T / vt[:, 2]).T
errs = np.sum((xys - proj)**2, axis=1)
print np.sum(errs)

# setup tensorflow
import tensorflow as tf
#fake_guess = np.zeros_like(guess)
#fake_guess[len(frms):, 2] = 1.0
t = tf.Variable(guess, dtype=tf.float32)
selects_tf = tf.placeholder(tf.float32, [None, len(frms)+len(good_good_tracks)])
xys_tf = tf.placeholder(tf.float32, [None, 2])
mask_tf = tf.placeholder(tf.float32, [None])

vt = tf.matmul(selects_tf, t)
proj = tf.transpose(tf.transpose(vt[:, 0:2]) / vt[:, 2])
cost = tf.reduce_sum(tf.transpose(((xys_tf - proj)**2)) * mask_tf)

optimizer = tf.train.AdamOptimizer().minimize(cost)

init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)

mask = np.array([1.0] * (selects.shape[0]))

# run for 100 epochs
for i in range(100):
  ret = sess.run([optimizer, cost], feed_dict={selects_tf: selects, xys_tf: xys, mask_tf: mask})
  
  if i%10 == 0:
    # do ransac
    vt = np.dot(selects, t.value().eval(session=sess))
    proj = (vt[:, 0:2].T / vt[:, 2]).T
    errs = np.sum((xys - proj)**2, axis=1)
    mask[np.where(errs>0.00001)] = 0.0
    print i, ret, sum(mask)
    
tfout = t.value().eval(session=sess)
tfrts = np.zeros((len(frms),3,4))
tfrts[:, :, 3] = tfout[0:len(frms)]
tfrts[:, 0, 0] = 1.0
tfrts[:, 1, 1] = 1.0
tfrts[:, 2, 2] = 1.0
tfpts = tfout[len(frms):]
tfrts.shape, tfpts.shape
#plot(tfrts[:, :, 3])

Hi George,

Was googling some words like "tensorflow and bundle adjustment" and find only this page. Just wondering how this "low quality bundle adjustment "is working in your experiments?

Thanks, Bo