SNE-RoadSeg
SNE-RoadSeg for Freespace Detection in PyTorch, ECCV 2020 (by hlwang1124)
Three-Filters-to-Normal
Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator (RAL+ICRA'21) (by ruirangerfan)
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| SNE-RoadSeg | Three-Filters-to-Normal | |
|---|---|---|
| 1 | 1 | |
| 290 | 95 | |
| - | - | |
| 1.8 | 1.8 | |
| almost 3 years ago | over 2 years ago | |
| Python | C++ | |
| MIT License | MIT License |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
SNE-RoadSeg
Posts with mentions or reviews of SNE-RoadSeg.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-29.
-
[R] Computer Vision for Driving Scene Understanding: from Autonomous Driving to Road Condition Assessment - Link to a free online lecture by the author in comments
Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation - https://arxiv.org/abs/2012.07890 git: https://github.com/hlwang1124/SNE-RoadSeg
Three-Filters-to-Normal
Posts with mentions or reviews of Three-Filters-to-Normal.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-08-29.
-
[R] Computer Vision for Driving Scene Understanding: from Autonomous Driving to Road Condition Assessment - Link to a free online lecture by the author in comments
git: https://github.com/ruirangerfan/Three-Filters-to-Normal
What are some alternatives?
When comparing SNE-RoadSeg and Three-Filters-to-Normal you can also consider the following projects:
3DDFA_V2 - The official PyTorch implementation of Towards Fast, Accurate and Stable 3D Dense Face Alignment, ECCV 2020.
unsupervised_disparity_map_segmentation - Road Damage Detection Based on Unsupervised Disparity Map Segmentation (T-ITS)
Ultra-Fast-Lane-Detection - Ultra Fast Structure-aware Deep Lane Detection (ECCV 2020)
rethinking_road_reconstruction_pothole_detection - Rethinking Road Surface 3D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation (T-CYB)
napkinXC - Extremely simple and fast extreme multi-class and multi-label classifiers.
road_surface_3d_reconstruction_datasets - Road Surface 3D Reconstruction Based on Dense Subpixel Disparity Map Estimation (T-IP)
SNE-RoadSeg vs 3DDFA_V2
Three-Filters-to-Normal vs unsupervised_disparity_map_segmentation
SNE-RoadSeg vs Ultra-Fast-Lane-Detection
Three-Filters-to-Normal vs rethinking_road_reconstruction_pothole_detection
SNE-RoadSeg vs rethinking_road_reconstruction_pothole_detection
Three-Filters-to-Normal vs napkinXC
SNE-RoadSeg vs road_surface_3d_reconstruction_datasets