def __init__(self,
position,
look_at,
up,
fov,
clip_near,
resolution,
fisheye=False):
assert (position.dtype == torch.float32)
assert (len(position.shape) == 1 and position.shape[0] == 3)
assert (look_at.dtype == torch.float32)
assert (len(look_at.shape) == 1 and look_at.shape[0] == 3)
assert (up.dtype == torch.float32)
assert (len(up.shape) == 1 and up.shape[0] == 3)
assert (fov.dtype == torch.float32)
assert (len(fov.shape) == 1 and fov.shape[0] == 1)
assert (isinstance(clip_near, float))
self.position = position
self.look_at = look_at
self.up = up
self.fov = fov
self.cam_to_world = transform.gen_look_at_matrix(position, look_at, up)
self.world_to_cam = torch.inverse(self.cam_to_world).contiguous()
self.fov_factor = torch.tan(transform.radians(0.5 * fov))
self.clip_near = clip_near
self.resolution = resolution
self.fisheye = fisheye
def __init__(self,
position,
look_at,
up,
fov,
clip_near,
resolution,
fisheye=False):
self.position = position
self.look_at = look_at
self.up = up
self.fov = fov
self.cam_to_world = transform.gen_look_at_matrix(position, look_at, up)
self.world_to_cam = torch.inverse(self.cam_to_world)
self.fov_factor = torch.tan(transform.radians(0.5 * fov))
self.clip_near = clip_near
self.resolution = resolution
self.fisheye = fisheye
def __init__(self,
position,
look_at,
up,
fov,
clip_near,
resolution,
cam_to_ndc=None,
fisheye=False):
assert (position.dtype == tf.float32)
assert (len(position.shape) == 1 and position.shape[0] == 3)
assert (look_at.dtype == tf.float32)
assert (len(look_at.shape) == 1 and look_at.shape[0] == 3)
assert (up.dtype == tf.float32)
assert (len(up.shape) == 1 and up.shape[0] == 3)
if fov is not None:
assert (fov.dtype == tf.float32)
assert (len(fov.shape) == 1 and fov.shape[0] == 1)
assert (isinstance(clip_near, float))
self._position = position
self._look_at = look_at
self._up = up
self._fov = fov
self.cam_to_world = transform.gen_look_at_matrix(position, look_at, up)
self.world_to_cam = tf.linalg.inv(self.cam_to_world).contiguous()
if cam_to_ndc is None:
fov_factor = 1.0 / tf.tan(transform.radians(0.5 * fov))
o = tf.ones([1], dtype=tf.float32)
diag = tf.concat([fov_factor, fov_factor, o], 0)
self._cam_to_ndc = tf.diag(diag)
else:
self._cam_to_ndc = cam_to_ndc
self.ndc_to_cam = tf.linalg.inv(self.cam_to_ndc)
self.clip_near = clip_near
self.resolution = resolution
self.fisheye = fisheye
def up(self, value):
self._up = value
self.cam_to_world = \
transform.gen_look_at_matrix(self._position, self._look_at, self._up)
self.world_to_cam = tf.linalg.inv(self.cam_to_world).contiguous()
def look_at(self, value):
self._look_at = value
self.cam_to_world = \
transform.gen_look_at_matrix(self._position, self._look_at, self._up)
self.world_to_cam = torch.inverse(self.cam_to_world).contiguous()