Comments (2)
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For the ATE evaluation, we've focused on the TUM dataset as the standard benchmark and selected sequences to compare against the results published in the CNN-SLAM and DeepTAM papers. The code for these methods was not available to run. At the time, joint mapping and tracking implementation was not published for DeepTAM and CNN-SLAM is still not open source. Therefore, in the reconstruction evaluation table we've focused on reconstruction only.
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The reference to SfMLearner was to show that we follow the principle of scaling depth maps to compare them against the ground truth. We did not use the ground truth median as SfMLearner though, but for a more fair comparison we've used the 'optimal' scale estimated by the TUM script.
Comparing monocular SLAM systems is indeed hard, and there is a lot more experiments or axes we could compare on. That's why we will be releasing the full code together with the evaluation.
from deepfactors.
For the ATE evaluation, we've focused on the TUM dataset as the standard benchmark and selected sequences to compare against the results published in the CNN-SLAM and DeepTAM papers. The code for these methods was not available to run. At the time, joint mapping and tracking implementation was not published for DeepTAM and CNN-SLAM is still not open source. Therefore, in the reconstruction evaluation table we've focused on reconstruction only.
What I meant is that you can have both the reconstruction accuracy and the ATE evaluations in Table II, similar to Table 1 in CNN-SLAM. I am curious to find out if the reconstruction accuracy is proportional to the camera trajectory accuracy.
Edit: The reason why this is interesting is that you are using graph-based optimisation, which supposedly should give you better mapping and tracking performance; in contrast, CNN-SLAM uses alternating optimisation (tracking and then EKF-style depth fusion), which potentially can give you good tracking but bad mapping, or vice versa (compare ICL/office2 and ICL/living1 in Table 1 of CNN-SLAM).
The reference to SfMLearner was to show that we follow the principle of scaling depth maps to compare them against the ground truth. We did not use the ground truth median as SfMLearner though, but for a more fair comparison we've used the 'optimal' scale estimated by the TUM script.
Got it. That makes sense.
Thanks again.
from deepfactors.
Related Issues (20)
- Conv2DCustomBackpropInputOp only supports NHWC HOT 8
- Saving the results HOT 1
- Compiling third-party dependencies does not work correctly HOT 4
- Source Webcam HOT 1
- monocular camera HOT 1
- About pthread HOT 1
- OptimizeRep may never get removed HOT 2
- Do you plan to release the train code? HOT 1
- OpenGL Error : GLSL 3.30 is not supported HOT 1
- How to reproduce the same results on TUM sequences? HOT 1
- Error in your relative pose jacobian to pose0 and pose1 jacobian calculation HOT 1
- About vision_core
- Build Issue with cmake 3.20.5
- Build dependency library display error HOT 2
- Running session failed: Conv2DCustomBackpropInputOp only supports NHWC. HOT 3
- scripts/run_scannet.bash: line 102: 3011 Illegal instruction ${df_bin} --flagfile=data/flags/dataset_odom.flags --source_url=scannet://${seq_dir}
- error: invalid use of incomplete type βstruct Eigen::internal::enable_if<false, pangolin::OpenGlMatrix*>β HOT 1
- failed to load the pre training model HOT 1
- download_network.bash failed
- I encountered an error while compiling DeepFactors,does anyone know how to fix this?
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