Comments (4)
We set the batchsize of inferene at scripts/eval_dtu.sh
. If you set this in env.sh
, I doubt it would be overwritten by 2 since env.sh
is executed before.
For inference, 170 seconds are longer than our experiments, but considering 1060 is a relative old model, it makes sense to me, as long as the predicted depth map is correct.
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Thanks for your response. To answer you, the depth maps and fusion steps work fine based on the resulting .ply mesh.
Could you provide a few reference timings? I'd like to explore your technique but the timing I see is is orders of magnitude longer than MVS-Net and variants which unfortunately makes testing impractical.
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For DTU evaluation, the inference time for one image is around 40 seconds. Since you can increase the batchsize, the averaged inference time could be reduced to 20+ seconds. Considering the ideology that RNN regularization trades time for space, time is indeed a bottleneck.
from aa-rmvsnet.
Thank you @QT-Zhu for relaying a general sense of your evaluation times, and your elegant point that time and space are once again entangled properties. ;-) On a practical level, I'm eager to do a few tests on a more capable system.
I also want to express my thanks for packaging your code in a succinct, easy to follow way. It is one of the few deep learning projects that compiled the first time!
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Related Issues (20)
- Tanks and temples 结果 HOT 6
- Questions about testing BlendesMVS HOT 2
- The settings of fine-tune on BlendedMVS HOT 5
- NaN value in testing HOT 4
- Error when running testing HOT 2
- question about sample details? HOT 2
- About numdepth and interval_scale HOT 1
- About the pretrained model on BlendMVS HOT 1
- Parameters for torch summary
- Nan while using mvsnet_cls_loss in other framework HOT 2
- Where can I download the pre-trained model HOT 1
- Load your existing model to continue training HOT 3
- Dataloader tensor size error HOT 8
- How much memory is required for training HOT 1
- the finer DTU ground truth HOT 1
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- tank
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