Comments (5)
Hey Chris,
Thanks for point this out -- the comment was outdated, and we are indeed doing bottom crop (same as the KITTI dataset).
from self-supervised-depth-completion.
So does this mean that the adaptation of the intrinsics should be changed to:
K[0,2] = K[0,2] - 26 # from width = 1242 to 1216, with a 13-pixel cut on both sides
K[1,2] = K[1,2] - 23 # from width = 375 to 352, with a 11.5-pixel cut on both sides
I crop my image to owidth, oheight, and then scale it by a factor imScale. Thus I changed the code in the kitti_loader as such (with my own intrinsics):
orig_x, orig_y = 2208, 1242
K = np.zeros((3,3))
fx, fy, cx, cy = 1399.87, 1399.87, 1056.62, 597.53
K[0,0] = fx
K[0,2] = cx - (orig_x - owidth)/2
K[1,1] = fy
K[1,2] = cy - (orig_y - oheight)/2
K = scale*K
K[2,2] = 1
return K
and in main.py@L82 to:
kitti_intrinsics = Intrinsics(int(owidth*imScale), int(oheight*imScale), fu, fv, cu, cv).cuda()
Are there any other locations in the code where the intrinsics are used? I did not find any, but I am experiencing similar problems to #19
from self-supervised-depth-completion.
Are there any other locations in the code where the intrinsics are used?
There is an Intrinsics class in inverse_warp.py that performs similarly to your code.
If you use VLP-32 as input, it is not surprising that the pretrained model does not work (since it was trained on HDL-64 lidars). Some finetuning on your own dataset might be necessary.
from self-supervised-depth-completion.
# note: we will take the center crop of the images during augmentation
# that changes the optical centers, but not focal lengths
K[0, 2] = K[
0,
2] - 13 # from width = 1242 to 1216, with a 13-pixel cut on both sides
K[1, 2] = K[
1,
2] - 11.5 # from width = 375 to 352, with a 11.5-pixel cut on both sides
so the code should be changed ?
from self-supervised-depth-completion.
When doing BottomCrop, I think it should be:
K[0, 2] = K[0, 2] - 13
K[1, 2] = K[1, 2] - 23
I don't know if it is correct.
Thanks!
from self-supervised-depth-completion.
Related Issues (20)
- Error while loading "calib_cam_to_cam.txt" - can not reshape the array.
- question about depth-estimation results HOT 2
- What is the network used for single d?
- Why I can't get the result when using the trained model you provided?
- How can I get the result in your paper?
- About extracting trained model HOT 2
- Clip output in model.py
- inference HOT 2
- colorize the depth map HOT 1
- some problem about photometric_loss
- Use your pretrained model: GPU run out of memory. 8.95 gb already allocated
- Save output depth map HOT 1
- dataset extracting
- Training doesn't converge HOT 4
- silog error measurement
- Running Error in train mode sparse+photo HOT 1
- To much warning. HOT 2
- Use Stereo Pair Instead of Temporal Pair for Self-Supervised Training?
- The result cannot be reproduced
- Some questions about the details of the code
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 self-supervised-depth-completion.