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Add GNC rotation averaging #759
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8ad3453
Add GNC rotation averaging
travisdriver 97d930f
Update unified config
travisdriver f3014bd
Add combined Shonan and GNC
travisdriver 08d1195
Switch to combined Shonan and GNC
travisdriver f313c86
Increase num_matched to 10
travisdriver 20dd5ef
Initialize GNC with MST
travisdriver 0316797
Debugging MST
travisdriver 4dc91c3
Remove MST max iterations
travisdriver 1cff18c
Use Shonan weighted by # inliers
travisdriver 5fb4c37
Increase max p to 64
travisdriver 93aa0b2
Use Huber loss
travisdriver 883258e
Use Huber
travisdriver 301be6b
Weight GNC with matches
travisdriver 8842c5d
Dummy push to make CI run
travisdriver 17876b8
IW-Shonan w/ Huber
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"""Shonan Rotation Averaging. | ||
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The algorithm was proposed in "Shonan Rotation Averaging:Global Optimality by | ||
Surfing SO(p)^n" and is implemented by wrapping up over implementation provided | ||
by GTSAM. | ||
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References: | ||
- https://arxiv.org/abs/2008.02737 | ||
- https://gtsam.org/ | ||
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Authors: Jing Wu, Ayush Baid, John Lambert | ||
""" | ||
from typing import Dict, List, Optional, Set, Tuple | ||
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import gtsam | ||
import numpy as np | ||
import scipy | ||
from gtsam import ( | ||
BetweenFactorPose3, | ||
BetweenFactorPose3s, | ||
Pose3, | ||
Rot3, | ||
ShonanAveraging3, | ||
GncLMOptimizer, | ||
GncLMParams | ||
) | ||
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import gtsfm.utils.logger as logger_utils | ||
from gtsfm.averaging.rotation.rotation_averaging_base import RotationAveragingBase | ||
from gtsfm.common.pose_prior import PosePrior | ||
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ROT3_DOF = 3 | ||
POSE3_DOF = 6 | ||
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logger = logger_utils.get_logger() | ||
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_DEFAULT_TWO_VIEW_ROTATION_SIGMA = 1.0 | ||
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class GncRotationAveraging(RotationAveragingBase): | ||
"""Performs Shonan rotation averaging.""" | ||
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def __init__(self, two_view_rotation_sigma: float = _DEFAULT_TWO_VIEW_ROTATION_SIGMA) -> None: | ||
"""Initializes module. | ||
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Note: `p_min` and `p_max` describe the minimum and maximum relaxation rank. | ||
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Args: | ||
two_view_rotation_sigma: Covariance to use (lower values -> more strictly adhere to input measurements). | ||
""" | ||
self._two_view_rotation_sigma = two_view_rotation_sigma | ||
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def __get_gnc_params(self) -> GncLMParams: | ||
params = GncLMParams() | ||
return params | ||
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def __get_shonan_params(self) -> gtsam.ShonanAveragingParameters3: | ||
lm_params = gtsam.LevenbergMarquardtParams.CeresDefaults() | ||
shonan_params = gtsam.ShonanAveragingParameters3(lm_params) | ||
shonan_params.setUseHuber(False) | ||
shonan_params.setCertifyOptimality(True) | ||
return shonan_params | ||
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def __graph_from_2view_relative_rotations( | ||
self, i2Ri1_dict: Dict[Tuple[int, int], Rot3], old_to_new_idxs: Dict[int, int] | ||
) -> BetweenFactorPose3s: | ||
"""Create between factors from relative rotations computed by the 2-view estimator.""" | ||
# TODO: how to weight the noise model on relative rotations compared to priors? | ||
noise_model = gtsam.noiseModel.Isotropic.Sigma(ROT3_DOF, self._two_view_rotation_sigma) | ||
between_factors = gtsam.NonlinearFactorGraph() | ||
# graph.addPriorRot3(gtsam.symbol("R", 0), gtsam.Rot3(np.eye(3)), sigma_R0) | ||
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for (i1, i2), i2Ri1 in i2Ri1_dict.items(): | ||
if i2Ri1 is not None: | ||
i2_ = old_to_new_idxs[i2] | ||
i1_ = old_to_new_idxs[i1] | ||
between_factors.add(gtsam.BetweenFactorRot3(i2_, i1_, i2Ri1, noise_model)) | ||
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return between_factors | ||
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def __between_factors_from_2view_relative_rotations( | ||
self, i2Ri1_dict: Dict[Tuple[int, int], Rot3], old_to_new_idxs: Dict[int, int] | ||
) -> BetweenFactorPose3s: | ||
"""Create between factors from relative rotations computed by the 2-view estimator.""" | ||
# TODO: how to weight the noise model on relative rotations compared to priors? | ||
noise_model = gtsam.noiseModel.Isotropic.Sigma(POSE3_DOF, self._two_view_rotation_sigma) | ||
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between_factors = BetweenFactorPose3s() | ||
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for (i1, i2), i2Ri1 in i2Ri1_dict.items(): | ||
if i2Ri1 is not None: | ||
# ignore translation during rotation averaging | ||
i2Ti1 = Pose3(i2Ri1, np.zeros(3)) | ||
i2_ = old_to_new_idxs[i2] | ||
i1_ = old_to_new_idxs[i1] | ||
between_factors.append(BetweenFactorPose3(i2_, i1_, i2Ti1, noise_model)) | ||
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return between_factors | ||
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def _between_factors_from_pose_priors( | ||
self, i1Ti2_priors: Dict[Tuple[int, int], PosePrior], old_to_new_idxs: Dict[int, int] | ||
) -> BetweenFactorPose3s: | ||
"""Create between factors from the priors on relative poses.""" | ||
between_factors = BetweenFactorPose3s() | ||
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def get_isotropic_noise_model_sigma(covariance: np.ndarray) -> float: | ||
"""Get the sigma to be used for the isotropic noise model. | ||
We compute the average of the diagonal entries of the covariance matrix. | ||
""" | ||
avg_cov = np.average(np.diag(covariance), axis=None) | ||
return np.sqrt(avg_cov) | ||
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for (i1, i2), i1Ti2_prior in i1Ti2_priors.items(): | ||
i1_ = old_to_new_idxs[i1] | ||
i2_ = old_to_new_idxs[i2] | ||
noise_model_sigma = get_isotropic_noise_model_sigma(i1Ti2_prior.covariance) | ||
noise_model = gtsam.noiseModel.Isotropic.Sigma(POSE3_DOF, noise_model_sigma) | ||
between_factors.append(BetweenFactorPose3(i2_, i1_, i1Ti2_prior.value, noise_model)) | ||
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return between_factors | ||
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def _run_with_consecutive_ordering( | ||
self, | ||
num_connected_nodes: int, | ||
graph: gtsam.NonlinearFactorGraph, | ||
between_factors: BetweenFactorPose3s, | ||
initial: gtsam.Values, | ||
) -> List[Optional[Rot3]]: | ||
"""Run the rotation averaging on a connected graph w/ N keys ordered consecutively [0,...,N-1]. | ||
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Note: GTSAM requires the N input nodes to be connected and ordered from [0 ... N-1]. | ||
Modifying GTSAM would require a major philosophical overhaul, so we perform the re-ordering | ||
here in a sort of "wrapper". See https://github.com/borglab/gtsam/issues/784 for more details. | ||
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Args: | ||
num_connected_nodes: Number of unique connected nodes (i.e. images) in the graph | ||
(<= the number of images in the dataset) | ||
between_factors: BetweenFactorPose3s created from relative rotations from 2-view estimator and the priors. | ||
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Returns: | ||
Global rotations for each **CONNECTED** camera pose, i.e. wRi, as a list. The number of entries in | ||
the list is `num_connected_nodes`. The list may contain `None` where the global rotation could | ||
not be computed (either underconstrained system or ill-constrained system). | ||
""" | ||
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logger.info("Running GNC rotation averaging...") | ||
#shonan = ShonanAveraging3(between_factors, self.__get_shonan_params()) | ||
#initial = shonan.initializeRandomly() | ||
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optimizer = GncLMOptimizer(graph, initial, self.__get_gnc_params()) | ||
result = optimizer.optimize() | ||
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wRi_list_consecutive = [None] * num_connected_nodes | ||
for i in range(num_connected_nodes): | ||
if result.exists(i): | ||
wRi_list_consecutive[i] = result.atRot3(i) | ||
logger.info(wRi_list_consecutive) | ||
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return wRi_list_consecutive | ||
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def _nodes_with_edges( | ||
self, i2Ri1_dict: Dict[Tuple[int, int], Optional[Rot3]], relative_pose_priors: Dict[Tuple[int, int], PosePrior] | ||
) -> Set[int]: | ||
"""Gets the nodes with edges which are to be modelled as between factors.""" | ||
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unique_nodes_with_edges = set() | ||
for (i1, i2) in i2Ri1_dict.keys(): | ||
unique_nodes_with_edges.add(i1) | ||
unique_nodes_with_edges.add(i2) | ||
for (i1, i2) in relative_pose_priors.keys(): | ||
unique_nodes_with_edges.add(i1) | ||
unique_nodes_with_edges.add(i2) | ||
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return unique_nodes_with_edges | ||
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def run_rotation_averaging( | ||
self, | ||
num_images: int, | ||
i2Ri1_dict: Dict[Tuple[int, int], Optional[Rot3]], | ||
i1Ti2_priors: Dict[Tuple[int, int], PosePrior], | ||
corr_idxs: Dict[Tuple[int, int], np.ndarray], | ||
) -> List[Optional[Rot3]]: | ||
"""Run the rotation averaging on a connected graph with arbitrary keys, where each key is a image/pose index. | ||
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Note: functions as a wrapper that re-orders keys to prepare a graph w/ N keys ordered [0,...,N-1]. | ||
All input nodes must belong to a single connected component, in order to obtain an absolute pose for each | ||
camera in a single, global coordinate frame. | ||
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Args: | ||
num_images: Number of images. Since we have one pose per image, it is also the number of poses. | ||
i2Ri1_dict: Relative rotations for each image pair-edge as dictionary (i1, i2): i2Ri1. | ||
i1Ti2_priors: Priors on relative poses. | ||
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Returns: | ||
Global rotations for each camera pose, i.e. wRi, as a list. The number of entries in the list is | ||
`num_images`. The list may contain `None` where the global rotation could not be computed (either | ||
underconstrained system or ill-constrained system), or where the camera pose had no valid observation | ||
in the input to run_rotation_averaging(). | ||
""" | ||
if len(i2Ri1_dict) == 0: | ||
logger.warning("Shonan cannot proceed: No cycle-consistent triplets found after filtering.") | ||
wRi_list = [None] * num_images | ||
return wRi_list | ||
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nodes_with_edges = sorted(list(self._nodes_with_edges(i2Ri1_dict, i1Ti2_priors))) | ||
old_to_new_idxes = {old_idx: i for i, old_idx in enumerate(nodes_with_edges)} | ||
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between_factors = self.__between_factors_from_2view_relative_rotations( | ||
i2Ri1_dict, old_to_new_idxes | ||
) | ||
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initial = initialize_mst(num_images, i2Ri1_dict, corr_idxs, old_to_new_idxes) | ||
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graph: gtsam.NonlinearFactorGraph = self.__graph_from_2view_relative_rotations( | ||
i2Ri1_dict, old_to_new_idxes | ||
) | ||
# between_factors.extend(self._between_factors_from_pose_priors(i1Ti2_priors, old_to_new_idxes)) | ||
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wRi_list_subset = self._run_with_consecutive_ordering( | ||
len(nodes_with_edges), graph, between_factors, initial | ||
) | ||
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wRi_list = [None] * num_images | ||
for remapped_i, original_i in enumerate(nodes_with_edges): | ||
wRi_list[original_i] = wRi_list_subset[remapped_i] | ||
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return wRi_list | ||
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def initialize_mst( | ||
num_images: int, | ||
i2Ri1_dict: Dict[Tuple[int, int], Optional[Rot3]], | ||
corr_idxs: Dict[Tuple[int, int], np.ndarray], | ||
old_to_new_idxs: Dict[int, int], | ||
) -> gtsam.Values: | ||
"""Initialize global rotations using the minimum spanning tree (MST).""" | ||
# Compute MST. | ||
row, col, data = [], [], [] | ||
for (i1, i2), i2Ri1 in i2Ri1_dict.items(): | ||
if i2Ri1 is None: | ||
continue | ||
row.append(i1) | ||
col.append(i2) | ||
data.append(-corr_idxs[(i1, i2)].shape[0]) | ||
logger.info(corr_idxs[(i1, i2)]) | ||
corr_adjacency = scipy.sparse.coo_array((data, (row, col)), shape=(num_images, num_images)) | ||
Tcsr = scipy.sparse.csgraph.minimum_spanning_tree(corr_adjacency) | ||
logger.info(Tcsr.toarray().astype(int)) | ||
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# Build global rotations from MST. | ||
# TODO (travisdriver): This is simple but very inefficient. Use something else. | ||
i_mst, j_mst = Tcsr.nonzero() | ||
logger.info(i_mst) | ||
logger.info(j_mst) | ||
edges_mst = [(i, j) for (i, j) in zip(i_mst, j_mst)] | ||
iR0_dict = {i_mst[0]: np.eye(3)} # pick the left index of the first edge as the seed | ||
# max_iters = num_images * 10 | ||
iter = 0 | ||
while len(edges_mst) > 0: | ||
i, j = edges_mst.pop(0) | ||
if i in iR0_dict: | ||
jRi = i2Ri1_dict[(i, j)].matrix() | ||
iR0 = iR0_dict[i] | ||
iR0_dict[j] = jRi @ iR0 | ||
elif j in iR0_dict: | ||
iRj = i2Ri1_dict[(i, j)].matrix().T | ||
jR0 = iR0_dict[j] | ||
iR0_dict[i] = iRj @ jR0 | ||
else: | ||
edges_mst.append((i, j)) | ||
iter += 1 | ||
# if iter >= max_iters: | ||
# logger.info("Reached max MST iters.") | ||
# assert False | ||
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# Add to Values object. | ||
initial = gtsam.Values() | ||
for i, iR0 in iR0_dict.items(): | ||
initial.insert(old_to_new_idxs[i], Rot3(iR0)) | ||
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return initial | ||
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Can we add a file with unit tests for this class?