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May i ask a relevant issue? what's the best accuracy that monodepth estimation can achieve from a distance of 100 m #4
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We never did evaluation for distance between 80 - 100m. For now, depth estimation is based on geometry constraint, with further distance a big error as nature, I think it's hard for now to get 88% accuracy. |
hi is me again , may i ask what the difference between original and improved dataset at Table3 ,which should be concerned ?
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发件人: "Fangget"<notifications@github.com>;
发送时间: 2020年2月25日(星期二) 下午2:11
收件人: "FangGet/PackNet-SFM-PyTorch"<PackNet-SFM-PyTorch@noreply.github.com>;
抄送: "渚薰岚"<849529792@qq.com>;"Author"<author@noreply.github.com>;
主题: Re: [FangGet/PackNet-SFM-PyTorch] May i ask a relevant issue? what's the best accuracy that monodepth estimation can achieve from a distance of 100 m (#4)
Closed #4.
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Not understand what you say, please make it more clearly. |
”Table 3: Quantitative performance comparison of PackNet-SfM on the KITTI dataset for distances up to 80m. For Abs Rel, Sq Rel, RMSE and RMSElog lower is better, and for δ < 1.25, δ < 1.252 and δ < 1.253 higher is better. In the Datasetcolumn,CS+KreferstopretrainingonCityScapes(CS)andfine-tuningonKITTI(K).Mreferstomethodsthattrain using monocular (M) images, and M+v refers to added velocity weak supervision (v), as shown in Section 3.2. ‡ indicates ImageNet[10]pretraining. Original uses raw depth maps from[14] for evaluation,and Improved uses annotated depth maps from [42]. At test-time, all monocular methods (M) scale estimated depths with median ground-truth LiDAR information. Velocity-scaled (M+v) and supervised (D) methods are not scaled in such way, since they are already metrically accurate.“
I means i do not understand that "Original uses raw depth maps from[14] for evaluation,and Improved uses annotateddepth maps from [42]."
what is the difference between raw depth maps and annotated depth maps ? it may refer to Sparsity Invariant CNNs.
…------------------ 原始邮件 ------------------
发件人: "Fangget"<notifications@github.com>;
发送时间: 2020年3月3日(星期二) 晚上11:03
收件人: "FangGet/PackNet-SFM-PyTorch"<PackNet-SFM-PyTorch@noreply.github.com>;
抄送: "渚薰岚"<849529792@qq.com>; "Author"<author@noreply.github.com>;
主题: Re: [FangGet/PackNet-SFM-PyTorch] May i ask a relevant issue? what's the best accuracy that monodepth estimation can achieve from a distance of 100 m (#4)
Not understand what you say, please make it more clearly.
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Sorry, the version of "packnet-sfm" paper I got has no information about your metioned "annotated depth maps". why not just refer to the given linked paper[14] and [42] for more information? |
I need a monodepth estimation to achieve at least 88% accuracy from 80 - 100m , may it possible for now ?
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