import tensorflow as tf

            tf.reshape(poses[0][i], [1, 1, 3, 3]),
            tf.reshape(poses[1][i], [1, 1, 3]), inverse=True) * 2.0
        samples_object = (samples_object * (29.0/32.0) / 2.0 + 0.5) * 32.0 - 0.5
        samples = tf.squeeze(samples_object)
        interpolated = trilinear.interpolate(sdf, samples)
        occupancy_value = tf.math.sign(tf.nn.relu(interpolated + self.tol))
        sdf_values += occupancy_value
        intersection = tf.reduce_sum(tf.math.sign(tf.nn.relu(sdf_values - 1)))
        if intersection > prev_intersection:
          prev_intersection = intersection
          num_collisions += 1
        status2 = False
        if status2:
          a = 1
          values = interpolated
          inter = tf.reshape(values, [self.resolution,
                                      self.resolution,
                                      self.resolution])
          inter = tf.transpose(tf.reduce_max(inter, axis=a))
          im = axs[fig_obj_count, 0].matshow(inter.numpy())
          plt.colorbar(im, ax=axs[fig_obj_count, 0])

          values = tf.math.sign(tf.nn.relu(interpolated + self.tol))
          inter = tf.reshape(values, [self.resolution,
                                      self.resolution,
                                      self.resolution])
          inter = tf.transpose(tf.reduce_max(inter, axis=a))
          im = axs[fig_obj_count, 1].matshow(inter.numpy())
          plt.colorbar(im, ax=axs[fig_obj_count, 1])

