Compare 3 more cases
Frank Dellaert
parent
ce196d962f
commit
360dc4138c
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@ -32,7 +32,7 @@ from gtsam import (
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K = gtsam.symbol_shorthand.K
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# Methods to compare
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methods = ["SimpleF", "Fundamental", "Essential+Ks", "Calibrated"]
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methods = ["SimpleF", "Fundamental", "Essential+Ks", "Essential+K", "Calibrated", "Binary+Ks", "Binary+K"]
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# Formatter function for printing keys
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@ -76,8 +76,8 @@ def simulate_data(points, poses, cal, rng, noise_std):
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return measurements
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# Function to compute ground truth matrices
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def compute_ground_truth(method, poses, cal):
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"""Function to compute ground truth edge variables."""
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E1 = EssentialMatrix.FromPose3(poses[0].between(poses[1]))
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E2 = EssentialMatrix.FromPose3(poses[0].between(poses[2]))
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F1 = FundamentalMatrix(cal.K(), E1, cal.K())
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@ -90,58 +90,80 @@ def compute_ground_truth(method, poses, cal):
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SF1 = SimpleFundamentalMatrix(E1, f, f, c, c)
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SF2 = SimpleFundamentalMatrix(E2, f, f, c, c)
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return SF1, SF2
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elif method == "Essential+Ks" or method == "Calibrated":
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return E1, E2
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else:
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raise ValueError(f"Unknown method {method}")
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return E1, E2
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def build_factor_graph(method, num_cameras, measurements, cal):
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"""build the factor graph"""
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graph = NonlinearFactorGraph()
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# Determine the FactorClass based on the method
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if method == "Fundamental":
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FactorClass = gtsam.TransferFactorFundamentalMatrix
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elif method == "SimpleF":
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FactorClass = gtsam.TransferFactorSimpleFundamentalMatrix
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elif method == "Essential+Ks":
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elif method in ["Essential+Ks", "Essential+K"]:
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FactorClass = gtsam.EssentialTransferFactorKCal3f
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# add priors on all calibrations:
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elif method == "Binary+K":
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FactorClass = gtsam.EssentialMatrixFactor4Cal3f
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elif method == "Binary+Ks":
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FactorClass = gtsam.EssentialMatrixFactor5Cal3f
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elif method == "Calibrated":
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FactorClass = gtsam.EssentialTransferFactorCal3f
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else:
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raise ValueError(f"Unknown method {method}")
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# Add priors on calibrations if necessary
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if method in ["Essential+Ks", "Binary+Ks"]:
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for i in range(num_cameras):
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model = gtsam.noiseModel.Isotropic.Sigma(1, 10.0)
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graph.addPriorCal3f(K(i), cal, model)
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elif method == "Calibrated":
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FactorClass = gtsam.EssentialTransferFactorCal3f
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# No priors on calibration needed
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else:
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raise ValueError(f"Unknown method {method}")
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elif method in ["Essential+K", "Binary+K"]:
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model = gtsam.noiseModel.Isotropic.Sigma(1, 10.0)
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graph.addPriorCal3f(K(0), cal, model)
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z = measurements # shorthand
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for a in range(num_cameras):
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b = (a + 1) % num_cameras # Next camera
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c = (a + 2) % num_cameras # Camera after next
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# Vectors to collect tuples for each factor
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tuples1 = []
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tuples2 = []
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tuples3 = []
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# Collect data for the three factors
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for j in range(len(measurements[0])):
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tuples1.append((z[a][j], z[b][j], z[c][j]))
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tuples2.append((z[a][j], z[c][j], z[b][j]))
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tuples3.append((z[c][j], z[b][j], z[a][j]))
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# Add transfer factors between views a, b, and c.
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if method in ["Calibrated"]:
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(b, c), tuples1, cal))
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graph.add(FactorClass(EdgeKey(a, b), EdgeKey(b, c), tuples2, cal))
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(a, b), tuples3, cal))
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if method in ["Binary+Ks", "Binary+K"]:
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# Add binary Essential Matrix factors
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ab, ac = EdgeKey(a, b).key(), EdgeKey(a, c).key()
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for j in range(len(measurements[0])):
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if method == "Binary+Ks":
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graph.add(FactorClass(ab, K(a), K(b), z[a][j], z[b][j]))
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graph.add(FactorClass(ac, K(a), K(c), z[a][j], z[c][j]))
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else: # Binary+K
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graph.add(FactorClass(ab, K(0), z[a][j], z[b][j]))
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graph.add(FactorClass(ac, K(0), z[a][j], z[c][j]))
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else:
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(b, c), tuples1))
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graph.add(FactorClass(EdgeKey(a, b), EdgeKey(b, c), tuples2))
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(a, b), tuples3))
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# Add transfer factors between views a, b, and c
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# Vectors to collect tuples for each factor
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tuples1 = []
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tuples2 = []
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tuples3 = []
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for j in range(len(measurements[0])):
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tuples1.append((z[a][j], z[b][j], z[c][j]))
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tuples2.append((z[a][j], z[c][j], z[b][j]))
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tuples3.append((z[c][j], z[b][j], z[a][j]))
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# Add transfer factors between views a, b, and c.
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if method in ["Calibrated"]:
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(b, c), tuples1, cal))
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graph.add(FactorClass(EdgeKey(a, b), EdgeKey(b, c), tuples2, cal))
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(a, b), tuples3, cal))
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elif method == "Essential+K":
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(b, c), K(0), tuples1))
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graph.add(FactorClass(EdgeKey(a, b), EdgeKey(b, c), K(0), tuples2))
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(a, b), K(0), tuples3))
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else:
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(b, c), tuples1))
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graph.add(FactorClass(EdgeKey(a, b), EdgeKey(b, c), tuples2))
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graph.add(FactorClass(EdgeKey(a, c), EdgeKey(a, b), tuples3))
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return graph
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@ -159,22 +181,23 @@ def get_initial_estimate(method, num_cameras, ground_truth, cal):
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initialEstimate.insert(EdgeKey(a, b).key(), F1)
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initialEstimate.insert(EdgeKey(a, c).key(), F2)
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total_dimension += F1.dim() + F2.dim()
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elif method in ["Essential+Ks", "Calibrated"]:
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elif method in ["Essential+Ks", "Essential+K", "Binary+Ks", "Binary+K", "Calibrated"]:
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E1, E2 = ground_truth
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for a in range(num_cameras):
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b = (a + 1) % num_cameras # Next camera
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c = (a + 2) % num_cameras # Camera after next
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b = (a + 1) % num_cameras
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c = (a + 2) % num_cameras
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initialEstimate.insert(EdgeKey(a, b).key(), E1)
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initialEstimate.insert(EdgeKey(a, c).key(), E2)
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total_dimension += E1.dim() + E2.dim()
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else:
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raise ValueError(f"Unknown method {method}")
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if method == "Essential+Ks":
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# Insert initial calibrations
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# Insert initial calibrations
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if method in ["Essential+Ks", "Binary+Ks"]:
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for i in range(num_cameras):
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initialEstimate.insert(K(i), cal)
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total_dimension += cal.dim()
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elif method in ["Essential+K", "Binary+K"]:
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initialEstimate.insert(K(0), cal)
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total_dimension += cal.dim()
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print(f"Total dimension of the problem: {total_dimension}")
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return initialEstimate
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@ -205,7 +228,7 @@ def compute_distances(method, result, ground_truth, num_cameras, cal):
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key_ab = EdgeKey(a, b).key()
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key_ac = EdgeKey(a, c).key()
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if method in ["Essential+Ks", "Calibrated"]:
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if method in ["Essential+Ks", "Essential+K", "Binary+Ks", "Binary+K", "Calibrated"]:
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E_est_ab = result.atEssentialMatrix(key_ab)
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E_est_ac = result.atEssentialMatrix(key_ac)
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@ -218,15 +241,18 @@ def compute_distances(method, result, ground_truth, num_cameras, cal):
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SF_est_ac = result.atSimpleFundamentalMatrix(key_ac).matrix()
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F_est_ab = FundamentalMatrix(SF_est_ab)
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F_est_ac = FundamentalMatrix(SF_est_ac)
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elif method == "Essential+Ks":
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# Retrieve calibrations from result:
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elif method in ["Essential+Ks", "Binary+Ks"]:
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# Retrieve calibrations from result for each camera
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cal_a = result.atCal3f(K(a))
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cal_b = result.atCal3f(K(b))
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cal_c = result.atCal3f(K(c))
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# Convert estimated EssentialMatrices to FundamentalMatrices
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F_est_ab = FundamentalMatrix(cal_a.K(), E_est_ab, cal_b.K())
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F_est_ac = FundamentalMatrix(cal_a.K(), E_est_ac, cal_c.K())
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elif method in ["Essential+K", "Binary+K"]:
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# Use single shared calibration
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cal_shared = result.atCal3f(K(0))
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F_est_ab = FundamentalMatrix(cal_shared.K(), E_est_ab, cal_shared.K())
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F_est_ac = FundamentalMatrix(cal_shared.K(), E_est_ac, cal_shared.K())
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elif method == "Calibrated":
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# Use ground truth calibration
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F_est_ab = FundamentalMatrix(cal.K(), E_est_ab, cal.K())
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