Linking pages
- GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets. https://github.com/caesar0301/awesome-public-datasets 34 comments
- GitHub - tatsuyah/vehicle-detection: Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree. https://github.com/tatsuyah/vehicle-detection 21 comments
- GitHub - awesomedata/awesome-public-datasets: A topic-centric list of HQ open datasets. https://github.com/awesomedata/awesome-public-datasets 2 comments
- Comma.ai open-sources the data it used for its first successful driverless trips | TechCrunch https://techcrunch.com/2016/08/03/comma-ai-open-sources-the-data-it-used-for-its-first-successful-driverless-trips/ 2 comments
- GitHub - alexklwong/learning-topology-synthetic-data: Tensorflow implementation of Learning Topology from Synthetic Data for Unsupervised Depth Completion (RAL 2021 & ICRA 2021) https://github.com/alexklwong/learning-topology-synthetic-data 1 comment
- KITTI-360 http://www.cvlibs.net/datasets/kitti-360/ 1 comment
- Improving the Photorealism of Driving Simulations with Generative Adversarial Networks - Unite.AI https://www.unite.ai/improving-the-photorealism-of-driving-simulations-with-generative-adversarial-networks/ 0 comments
- How we Scale Machine Learning - Scale https://scale.com/blog/how-we-scale-machine-learning 0 comments
- A Structured Approach to Unsupervised Depth Learning from Monocular Videos – Google AI Blog https://ai.googleblog.com/2018/11/a-structured-approach-to-unsupervised.html 0 comments
- UrbanScene3D: Semantically Labeled Cityscapes for Autonomous Vehicle Research - Unite.AI https://www.unite.ai/urbanscene3d-semantically-labeled-cityscapes-for-autonomous-vehicle-research/ 0 comments
- GitHub - msadowski/awesome-weekly-robotics: A list of projects that were or will be featured in Weekly Robotics newsletter https://github.com/msadowski/awesome-weekly-robotics 0 comments
- Some Methods of Gradient Estimation Are Better Than Others http://wet-robots.ghost.io/some-methods-of-gradient-estimation-are-better-than-others/ 0 comments
- How to play with autonomous driving without a car | by Michał Górnik | Tooploox | Medium https://medium.com/tooploox/how-to-play-with-autonomous-driving-without-a-car-458891416a49 0 comments
- Research Guide for Depth Estimation with Deep Learning | by Derrick Mwiti | Heartbeat https://heartbeat.fritz.ai/research-guide-for-depth-estimation-with-deep-learning-1a02a439b834 0 comments
- Introducing Argoverse: data and HD maps for computer vision and machine learning research to advance self-driving technology | by Argo AI | Medium https://medium.com/@ArgoAI/introducing-argoverse-data-and-hd-maps-for-computer-vision-and-machine-learning-research-to-fcf2a072b05 0 comments
- GitHub - gsurma/mono_depth_estimator: Mono depth estimation for self-driving cars 🚗 https://github.com/gsurma/mono_depth_estimator 0 comments
- GitHub - hlzz/DeepMatchVO: Implementation of ICRA 2019 paper: Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation https://github.com/hlzz/DeepMatchVO 0 comments
- Classifying Vehicles & Pedestrians in a Point Cloud | by Michael Gump | Voyage https://news.voyage.auto/classifying-vehicles-pedestrians-in-point-cloud-83db7cd6192e 0 comments
- GitHub - opencv/cvat: Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale. https://github.com/opencv/cvat 0 comments
- GitHub - ErkanMilli/3MT-RoadSeg: Multi-Modal Multi-Task (3MT) Road Segmentation, IEEE RA-L 2023 https://github.com/ErkanMilli/3MT-RoadSeg 0 comments
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