Comments (11)
I think you aligned the sparse point clouds with the lidar scan to compute the F1 score. I wonder if it may relate to the SfM results' point density. Can you provide more details on the metrics between the incremental reconstruction and DAGSfM? e.g. the projection errors, number of recovered camera poses, and 3D points. Moreover, you may also need to show the reconstruction results through the modified GUI (The splitter camera poses and sparse point clouds) to inspect the visual artifacts.
from dagsfm.
I think you aligned the sparse point clouds with the lidar scan to compute the F1 score. I wonder if it may relate to the SfM results' point density. Can you provide more details on the metrics between the incremental reconstruction and DAGSfM? e.g. the projection errors, number of recovered camera poses, and 3D points. Moreover, you may also need to show the reconstruction results through the modified GUI (The splitter camera poses and sparse point clouds) to inspect the visual artifacts.
Actually, we aligned the reconstructed dense pointclouds with lidar pointcloud to compute the F1 score. It mixed the SfM results with dense reconstruction. So it's a bit difficult to compare. Here is our dense pointcloud results. As you can see, they share the same world coordinates, but at some point, the distributed reconstruction result drift a bit.
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction.
The info of distributed reconstruction:
from dagsfm.
I think you aligned the sparse point clouds with the lidar scan to compute the F1 score. I wonder if it may relate to the SfM results' point density. Can you provide more details on the metrics between the incremental reconstruction and DAGSfM? e.g. the projection errors, number of recovered camera poses, and 3D points. Moreover, you may also need to show the reconstruction results through the modified GUI (The splitter camera poses and sparse point clouds) to inspect the visual artifacts.
Actually, we aligned the reconstructed dense pointclouds with lidar pointcloud to compute the F1 score. It mixed the SfM results with dense reconstruction. So it's a bit difficult to compare. Here is our dense pointcloud results. As you can see, they share the same world coordinates, but at some point, the distributed reconstruction result drift a bit.
![]()
![]()
I think it may be sourced from the inaccuracy of the final alignment step of our DAGSfM. As pointed out in our recent paper AdaSfM: From Coarse Global to Fine Incremental Adaptive Structure from Motion, DAGSfM may suffer from its final alignment stage, especially when matching outliers exist. AdaSfM solved this by introducing priors from global SfM. If you already have lidar scans, then you already have global priors. Then I would like to suggest align the SfM results to the coordinate frame of the lidar scans.
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
The scene looks not such large. To make the reconstruciton better, you may try to increase the upperbound from 500 to 700, which decreases the block number from 3 to 2.
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
Could you visualize the camera poses with the modified GUI in DAGSfM since it can clearly show which camera poses belong to the same cluster.
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
The scene looks not such large. To make the reconstruciton better, you may try to increase the upperbound from 500 to 700, which decreases the block number from 3 to 2.
Yes, I'm trying it now. Later I'll shared the result with you. Thanks
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
The scene looks not such large. To make the reconstruciton better, you may try to increase the upperbound from 500 to 700, which decreases the block number from 3 to 2.
The good news is that the F-score is almost the same as incremental after I changed the block number from 3 to 2. But the BA cost time is more than incremental reconstruction (I only tested once here).
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
Could you visualize the camera poses with the modified GUI in DAGSfM since it can clearly show which camera poses belong to the same cluster.
where can I find the guideline about the visualization process ?
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
The scene looks not such large. To make the reconstruciton better, you may try to increase the upperbound from 500 to 700, which decreases the block number from 3 to 2.
The good news is that the F-score is almost the same as incremental after I changed the block number from 3 to 2. But the BA cost time is more than incremental reconstruction (I only tested once here).
The speed of DAGSfM should be comparable to the original COLMAP when the scene scale is not large (e.g. less than 3000 images in aerial scenes or 2000 images for sequential dataset). It can outperform COLMAP when processing 5000 images or even larger (you can refer to some issues that someone used DAGSfM to reconstruct scenes with about 100K images). Moreover, you can run DAGSfM in distributed mode to improve the performance.
from dagsfm.
Here are additional info:
The BA result:
upper one is global BA of distributed reconstruction, another is incremental reconstruction. The info of distributed reconstruction:
Could you visualize the camera poses with the modified GUI in DAGSfM since it can clearly show which camera poses belong to the same cluster.
where can I find the guideline about the visualization process ?
I think you used the original COLMAP's GUI for visualization. I made minor modifications to COLMAP's GUI, where each image has an additional cluster_id
property. Therefore, images that belong to the same group will be rendered in the same color. You should use the GUI of DAGSfM and import your model as using COLMAP's GUI.
from dagsfm.
Related Issues (20)
- Sparse reconstruction gone awry HOT 17
- Global SfM available? HOT 2
- only one worker run in distributed mode HOT 5
- after Merging Clusters ,coordinate error? HOT 3
- distributed mode ? empty file HOT 3
- BLAS library install
- Image pairs:0 HOT 5
- issue of the workflow for similarity graph build and featrue extraction HOT 1
- Issue about Sequential Mode HOT 1
- Sequential mode error HOT 7
- Can't run pipeline with sequential mode. HOT 5
- ERROR: Command `point_cloud_segmenter` not recognized. To list the available commands, run `colmap help`.
- progress item HOT 1
- I have a problem that if I run the distributed_sfm.sh script directly, it throws an exception, but if I run the feature extraction and feature matching script first and then run distributed_sfm.sh, it works fine, is this reasonable?
- Custom feature points/matches HOT 1
- Reimplementation of AdaSfM
- The number of registered images is too few
- undefined reference to symbol 'cholmod_solve'
- Distributed reconstruction failed!
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from dagsfm.