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Summary: Efficient PnP algorithm to fit 2D to 3D correspondences under perspective assumption. Benchmarked both variants of nullspace and pick one; SVD takes 7 times longer in the 100K points case. Reviewed By: davnov134, gkioxari Differential Revision: D20095754 fbshipit-source-id: 2b4519729630e6373820880272f674829eaed073
26 lines
673 B
Python
26 lines
673 B
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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import itertools
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from fvcore.common.benchmark import benchmark
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from test_perspective_n_points import TestPerspectiveNPoints
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def bm_perspective_n_points() -> None:
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case_grid = {
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"batch_size": [1, 10, 100],
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"num_pts": [100, 100000],
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"skip_q": [False, True],
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}
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test_cases = itertools.product(*case_grid.values())
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kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
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test = TestPerspectiveNPoints()
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benchmark(
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test.case_with_gaussian_points,
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"PerspectiveNPoints",
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kwargs_list,
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warmup_iters=1,
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)
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