36 lines
1.6 KiB
Python
36 lines
1.6 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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def normalize_point_clouds(pcs, mode='shape_bbox'): # can be a property func
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'''
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copied from https://github.com/luost26/diffusion-point-cloud/blob/0bfd688379e78ac75fa75e6a2c5029e362496169/test_gen.py#L16
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Args:
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pcs: list of [N,3] or tensor in shape: B,N,3
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'''
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# logger.debug('Normalization mode: %s' % mode)
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assert(type(pcs) == list or len(pcs.shape) ==
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3), f'expect pcs to be list, get: {type(pcs)} or 3d tensor; '
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output_list = []
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for i in range(len(pcs)): # , desc='Normalize'):
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pc = pcs[i]
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pc = pc.detach().clone()
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assert(mode == 'shape_bbox')
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assert(len(pc.shape) == 2 and pc.shape[-1] in [3, 4,
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6, 9]), f'expect get (N,3 or 6), get {pc.shape}'
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pc_max, _ = pc.max(dim=0, keepdim=True) # (1, 3)
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pc_min, _ = pc.min(dim=0, keepdim=True) # (1, 3)
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pc_min = pc_min[:, :3]
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pc_max = pc_max[:, :3]
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shift = ((pc_min + pc_max) / 2).view(1, 3)
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scale = (pc_max - pc_min).max().reshape(1, 1) / 2
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pc[:, :3] = (pc[:, :3] - shift) / scale
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# pcs[i] = pc
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output_list.append(pc)
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return output_list
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