Comments (6)
Hi, it's good to know that you have improved the results a lot since last time you asked me.
sq_rel
is square relative error (|d_pred - d_gt| ^2 / d_gt
). I think this error may be reduced if you have smoother and more consistent depth predictions.
from lite-mono.
I am now closing this issue as there is no feedback.
from lite-mono.
I am now closing this issue as there is no feedback.
Thanks for the author's reply, I apologize for not seeing the message in time, I'll try the smoothing factor right away to see if it makes a difference.
I would also like to ask a question, does the author know anything about the relationship between relative depth and metric depth, I'm a bit confused now, how is the relative depth converted to metric depth, do they just differ by an unknown focal length?
from lite-mono.
@LLLYLong Just like other monocular self-supervised method, Lite-Mono can only predict relative depth. But in the evaluation we use median filter to scale the depth values.
from lite-mono.
@LLLYLong Just like other monocular self-supervised method, Lite-Mono can only predict relative depth. But in the evaluation we use median filter to scale the depth values.
@noahzn Scaling depth using median filtering also makes the depth value meaningful.
Am I to understand that there is a scaling factor difference between the relative depth and the metric depth.
That is, the network predicts relative depth, and if I specify a range of depths and scale the depth map, I get metric depth.
from lite-mono.
But in this way you cannot get very accurate depth. It also depends on the dataset you use.
from lite-mono.
Related Issues (20)
- Hi,I've got some new questions HOT 9
- Some question about the result HOT 4
- The edges of the image are foggy and blurry during the training process HOT 4
- Inexplicable "No such file or directory: 'Lite-Mono-main\\kitti_data\\\2011_09_26/2011_09_26_drive_0002_sync\\\image_02/data\\- 000000001.png'" HOT 2
- the CPU utilization has been very high, but the GPU utilization has been very low HOT 2
- training with Nuscenes dataset HOT 39
- Test outputs is black HOT 8
- I read your paper “For models trained from scratch an initial learning rate of 5e−4 with a cosine learning rate schedule [26] is adopted” But how should I implement it in the code? HOT 4
- How the reproduction of the results of the model achieves the results in the paper HOT 6
- Hello, my reproduction code is exactly the same as the code uploaded by the author of the paper, CUDA11.0, PyTorch 1.7.1. Please check your code and replace your CUDA and pytorch version.
- model reproduction HOT 11
- Hello, I have some questions regarding the model training with the KITTI dataset HOT 4
- Pre-training Weights HOT 1
- about pretrain acc HOT 1
- the results of training are black pictures HOT 5
- test code for pose HOT 2
- Subsequent issues with gt_depths.npz HOT 4
- sorry to bothering you HOT 1
- Output resolution problem HOT 5
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 lite-mono.