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https://github.com/facebookresearch/pytorch3d.git
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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
72 lines
2.0 KiB
Python
72 lines
2.0 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from copy import deepcopy
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from itertools import product
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from fvcore.common.benchmark import benchmark
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from test_points_alignment import TestCorrespondingPointsAlignment, TestICP
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def bm_iterative_closest_point() -> None:
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case_grid = {
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"batch_size": [1, 10],
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"dim": [3, 20],
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"n_points_X": [100, 1000],
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"n_points_Y": [100, 1000],
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"use_pointclouds": [False],
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}
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test_args = sorted(case_grid.keys())
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test_cases = product(*case_grid.values())
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kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
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# add the use_pointclouds=True test cases whenever we have dim==3
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kwargs_to_add = []
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for entry in kwargs_list:
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if entry["dim"] == 3:
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entry_add = deepcopy(entry)
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entry_add["use_pointclouds"] = True
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kwargs_to_add.append(entry_add)
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kwargs_list.extend(kwargs_to_add)
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benchmark(
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TestICP.iterative_closest_point,
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"IterativeClosestPoint",
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kwargs_list,
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warmup_iters=1,
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)
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def bm_corresponding_points_alignment() -> None:
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case_grid = {
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"allow_reflection": [True, False],
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"batch_size": [1, 10, 100],
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"dim": [3, 20],
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"estimate_scale": [True, False],
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"n_points": [100, 10000],
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"random_weights": [False, True],
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"use_pointclouds": [False],
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}
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test_args = sorted(case_grid.keys())
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test_cases = product(*case_grid.values())
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kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
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# add the use_pointclouds=True test cases whenever we have dim==3
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kwargs_to_add = []
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for entry in kwargs_list:
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if entry["dim"] == 3:
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entry_add = deepcopy(entry)
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entry_add["use_pointclouds"] = True
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kwargs_to_add.append(entry_add)
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kwargs_list.extend(kwargs_to_add)
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benchmark(
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TestCorrespondingPointsAlignment.corresponding_points_alignment,
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"CorrespodingPointsAlignment",
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kwargs_list,
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warmup_iters=1,
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)
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