Comments (2)
Q1. As we declared in our paper, the 1st baseline in Table 3 (Baseline(Reg)) is CasMVSNet in our implementation. It's well known that the quantitative metrics (acc., comp., overall) is heavily related to the fusion parameters, and we set the fusion parameters of all models in Table 3 to be the same (0.3 for confidence threshold and 3 for consistent view, same as RMVSNet), which is slightly different from the original CasMVSNet paper and get superior performance in overall metric.
Q2. It's indeed a thorny problem. We mitigate the drawback of these cases through our proposed unified focal loss to make the model pay more attention to these outliers. And we don't have statistical model performance at these outliers, but overall it's unimpeded.
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Thank you for reply.
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Related Issues (20)
- can not download DTU, Tank and Temples Dataset HOT 1
- Different results between released model and released ply HOT 3
- About abs_depthloss HOT 3
- 请问这个训练和测试时的源图像数量保持一致对结果有影响吗? HOT 2
- empty output point cloud HOT 9
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- About unity generation HOT 1
- 关于坦克和寺庙数据集的调参问题! HOT 2
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- 测试自己的数据集
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