Comments (10)
- Change the path parameters in the code
- Run BaseEvalMain_web.m
- Run ComputeStat_web.m
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In BaseEvalMain_web.m:
1.dataPath: download Points(6.3G) from http://roboimagedata.compute.dtu.dk/?page_id=36
2.plyPath: Mvsnet output path
3.resultsPath: BaseEvalMain_web.m output path
In ComputeStat_web.m:
1.dataPath: keeps the same path in BaseEvalMain_web.m
2.resultsPath:keeps the same path in BaseEvalMain_web.m
from mvsnet_pytorch.
In BaseEvalMain_web.m: 1.dataPath: download Points(6.3G) from http://roboimagedata.compute.dtu.dk/?page_id=36 2.plyPath: Mvsnet output path 3.resultsPath: BaseEvalMain_web.m output path
In ComputeStat_web.m: 1.dataPath: keeps the same path in BaseEvalMain_web.m 2.resultsPath:keeps the same path in BaseEvalMain_web.m
您好,我跑ComputeStat_web.m的结果是mean acc 0.4866 mean comp 0.3943,请问这个结果是论文中提到的最后结果吗?我不太理解ComputeStat_web.m和BaseEvalMain_web.m跑出来的结果分别代表什么意思,那个结果是论文中提到的结果呢,麻烦您指教一下可以吗,谢谢
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你可以再跑一遍,注意看那两个matlab文件在命令行跑出来的结果。
mean acc是指平均准确率,mean comp是指平均完整率,具体的评价指标在论文中有写,表示使用mvsnet模型训练后融合生成的点云和ground truth点云的之间的差别。
直观上的理解是这样的,0.4866 mean acc可以理解成:在生成的点云中,平均有48.66%的点深度和gt是一样的(在可接受的范围内);0.3943 mean comp可以理解成:生成的点云数量平均占gt的39.43%
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去看Read me的Results on DTU
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Deep Learning for Multi-view Stereo via Plane Sweep A Survey
我想你看完这一篇论文就都会了
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Deep Learning for Multi-view Stereo via Plane Sweep A Survey 我想你看完这一篇论文就都会了
好的
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Deep Learning for Multi-view Stereo via Plane Sweep A Survey 我想你看完这一篇论文就都会了
您好,请问提供的matlab代码中,哪一个matlab文件跑出来的结果是论文当中的acc和comp呀
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from mvsnet_pytorch.
@SHERRYMINE 请问如果我需要定量评价自己数据集的重建效果,需要怎么操作?如果使用这里给的matlab代码的话,那么obsmask文件是什么?该怎样生成?谢谢!
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