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hi,i also met that problem?have you solved this problem?thank you

> sorry. I'm late to check. Here's how I solved it : Installing Sophus https://github.com/tum-vision/tandem/tree/master/tandem/thirdparty/Sophus > > And! Check dependencies and reinstall cmake(version 3.24) cuda ubuntu version libeigen3-dev etc... >...

I also have some problems about depth loss.As you said,monocular depth output range in 0.01 to 0.04,should I normalization gt depth in this range.I use **nice_slam_apartment_to_monosdf.py** get Apartment dataset depth...

> @Wjt-shift If you use gt-depth, you don't need to use scale-invariant loss as it is designed to handle scale ambiguity in monocular depth. You could simply use L1 or...

> @Wjt-shift If you use gt-depth, you don't need to use scale-invariant loss as it is designed to handle scale ambiguity in monocular depth. You could simply use L1 or...

> @Wjt-shift The gt depth in replica is in range of [0, 6], so after your normalization, it should be in range around [0, 2] because the scene in normalized...

> Hi, after you divide the depth by 6553.5, you get the depth value in meters. But we further normalize the scene where the normalization factor is computed from the...

> > Hi, after you divide the depth by 6553.5, you get the depth value in meters. But we further normalize the scene where the normalization factor is computed from...

> Hi, depth from a monocular system is also up to scale, and in this case, you should use scale-invariant loss in MonoSDF. And if use different datasets,the constant 50...

I also met the same problem,And I try other version gtsam 4.1.1,4.2a8,4.2a7,but can't solve this problem,what public function should instead of optimizeDensely? have you solved it ?