Commit 6b6e2412 authored by Torsten Sattler's avatar Torsten Sattler

Update README.md

parent 44d16085
......@@ -17,18 +17,6 @@ We provide the following data for the challenge:
* a semantically annotated 3D point cloud depicting the area of the training sequence.
* A testing sequence consisting of calibrated images with their camera poses.
In order to submit to the challenge, please create a semantically annotated 3D triangle mesh from the test sequence.
The mesh should be stored in the [PLY text format](http://paulbourke.net/dataformats/ply/).
The file should store for each triangle a color corresponding to the triangle’s semantic class (see the calibrations/colors.yaml file for the mapping between semantic classes and colors).
Once you have created the mesh, please submit it using [this link](https://www.dropbox.com/request/23XzljBTn93zYl3ETjXn).
In addition, please send an email to torsten.sattler@inf.ethz.ch that includes the filename of the file you submitted as well as contact information.
We will evaluate the quality of the 3D meshes based on the completeness of the reconstruction, i.e., how much of the ground truth is covered, the accuracy of the reconstruction, i.e., how accurately the 3D mesh models the scene, and the semantic quality of the mesh, i.e., how close the semantics of the mesh are to the ground truth.
The deadline for submitting to the challenge is September 3rd (23:59 GMT).
For questions, please contact torsten.sattler@inf.ethz.ch.
## Data
* File `calibration/camchain-DDDD.yaml` - camera rig calibration
......@@ -68,13 +56,28 @@ The transformation from world to camera coordinates is given as `[R(q)|t]`, wher
## Evaluation
* Geometric accuracy of the reconstructed 3D point cloud (RMS error)
* Confusion matrix for 3D point semantic labels (overall and class accuracy %)
* Confusion matrix for 2D pixel semantic labels (overall and class accuracy %)
We will evaluate the following measures:
* Reconstruction accuracy in % for a set of distance thresholds (similar to [1,2])
* Reconstruction completeness in % for a set of distance thresholds (similar to [1,2])
* Semantic quality in % of the triangles that are correctly labeled.
We will use distance thresholds of 1cm, 2cm, 3cm, 5cm, and 10cm.
[1] Seitz et al., A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms, CVPR 2006
[2] Schöps et al., A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos, CVPR 2017
## Submission
See http://trimbot2020.webhosting.rug.nl/events/3drms/challenge/
In order to submit to the challenge, please create a semantically annotated 3D triangle mesh from the test sequence.
The mesh should be stored in the [PLY text format](http://paulbourke.net/dataformats/ply/).
The file should store for each triangle a color corresponding to the triangle’s semantic class (see the calibrations/colors.yaml file for the mapping between semantic classes and colors).
Once you have created the mesh, please submit it using [this link](https://www.dropbox.com/request/23XzljBTn93zYl3ETjXn).
In addition, please send an email to torsten.sattler@inf.ethz.ch that includes the filename of the file you submitted as well as contact information.
The deadline for submitting to the challenge is September 3rd (23:59 GMT).
## Questions
For questions, please contact torsten.sattler@inf.ethz.ch.
## Credits
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