diantaotu / acmh-acmm Goto Github PK
View Code? Open in Web Editor NEW对论文《Multi-Scale Geometric Consistency Guided Multi-View Stereo》的复现
License: MIT License
对论文《Multi-Scale Geometric Consistency Guided Multi-View Stereo》的复现
License: MIT License
我想试试完整程序可以吗?我联系过ADMM的作者,应该近两个月会有开源计划
Hi, thank you for sharing this code, I find it very useful to understand the original paper.
Do you know the author opened their code? https://github.com/GhiXu/ACMM
And he also opened ACMP, he said that combine ACMM and ACMP may get even better result, do you interseted in implementing it?
相关的两篇文章不是开源很久了吗,就在github上
亲测,一张1080p的图跑了三天。想请教一下作者,对于加速有没有什么比较好的方法?
my test code of time costs is as following:
def test(imgL, imgR):
model.eval()
with torch.no_grad():
imgL = Variable(torch.FloatTensor(imgL))
imgR = Variable(torch.FloatTensor(imgR))
imgL, imgR = imgL.cuda(), imgR.cuda()
for i in range(10):
start_time = time.time()
refined_disparity = model(imgL, imgR)
end_time = time.time()
print("time costs:{}".format(end_time - start_time))
return refined_disparity
and the outputs is :
image
my config is as :
config = {
"max_disp": 192,
"cost_aggregator_scale": 4, # for DeepPruner-fast change this to 8.
"mode": "evaluation", # for evaluation/ submission, change this to evaluation.
"feature_extractor_ca_level_outplanes": 32,
"feature_extractor_refinement_level_outplanes": 32, # for DeepPruner-fast change this to 64.
"feature_extractor_refinement_level_1_outplanes": 32,
"patch_match_args": {
"sample_count": 12,
"iteration_count": 2,
"propagation_filter_size": 3
},
"post_CRP_sample_count": 7,
"post_CRP_sampler_type": "uniform", #change to patch_match for Sceneflow model.
"hourglass_inplanes": 16
}
and the results is even accurate, why the time differs so greatly? And the first time runs faster 10 times then the later?
Environments:
GTX1080, python 3.5, Ubuntu16.04
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