Comments (2)
Indeed, it is fixed, to both a particular image size and a particular focal length.
If those are not meet, the network's result will be unpredictable.
However, during the training there was some augmentation where the image focal length would change a little, see here : https://github.com/ClementPinard/SfmLearner-Pytorch/blob/master/custom_transforms.py#L62
Note that image size never changes though
So even though the network itself is fully convolutional and thus would not crash when looking at a bigger image, the details in the image will not be the right size and thus the network will have a hard time getting depth right.
Even worse, I did a study where I would simply flip the image vertically (so the sky is at the bottom of the image) and the depth prediction was very bad too.
In conclusion, your best bet is to try to get your image to the training size (much smaller) or do a retraining of the network for bigger images, using the data module of this repo.
Hope it helped,
Clément
from sfmlearner-pytorch.
OK, thanks!
from sfmlearner-pytorch.
Related Issues (20)
- Large Errors on Pose Prediction Network HOT 3
- why the gpu memory cost of tensorflow version is larger than pytorch version HOT 2
- Weird results from pretrained model on KITTI images HOT 4
- Question about using oxts data HOT 1
- Cannot run `train.py` with nohup HOT 2
- imread during inference load the image as uint8 HOT 4
- How About the Flops, fps and parameter of this model? HOT 1
- regarding the predicted depth map during inverse warp HOT 2
- How to visualize the warped image (ref_img_wapred) HOT 2
- regarding inverse_warping HOT 10
- Question about diff
- How to load training dataset
- Regarding the depth used for generating target image HOT 5
- Question about the poses predicted by the posenet HOT 2
- about the pose scale HOT 2
- difference of the predicted translation and ground truth vectors HOT 7
- samples in test_files_eigen dont exist in the KITTI
- Seq 10 is not in the test_secens.txt HOT 2
- raw_data_downloading error HOT 3
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 sfmlearner-pytorch.