Displaying 1 to 15 from 15 results

pykitti - Python tools for working with KITTI data.

  •    Python

This package provides a minimal set of tools for working with the KITTI dataset [1] in Python. So far only the raw datasets and odometry benchmark datasets are supported, but we're working on adding support for the others. We welcome contributions from the community. This package assumes that you have also downloaded the calibration data associated with the sequences you want to work on (these are separate files from the sequences themselves), and that the directory structure is unchanged from the original structure laid out in the KITTI zip files.

kitti2bag - Convert KITTI dataset to ROS bag file the easy way!

  •    Python

This package enjoyed significant interest from more people then I could have thought at the beginning. I'm really glad to see that. I see many PRs and issues being raised but my day job does not allow me to push this repository further. In order to allow this package to prosper, I'm opening it up for the community. I'm more than happy to add you as a collaborator to this repository. Just send me an email. And by the way. To ensure we maintain the quality of the repo you are required to get the PR approval from at least one other collaborator. You can use the Gitter to communicate with others.

SuMa - Surfel-based Mapping for 3d Laser Range Data (SuMa)

  •    C++

Mapping of 3d laser range data from a rotating laser range scanner, e.g., the Velodyne HDL-64E. For representing the map, we use surfels that enables fast rendering of the map for point-to-plane ICP and loop closure detection. J. Behley, C. Stachniss. Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments, Proc. of Robotics: Science and Systems (RSS), 2018.




fastseg - 📸 PyTorch implementation of MobileNetV3 for real-time semantic segmentation, with pretrained weights & state-of-the-art performance

  •    Python

This respository aims to provide accurate real-time semantic segmentation code for mobile devices in PyTorch, with pretrained weights on Cityscapes. This can be used for efficient segmentation on a variety of real-world street images, including datasets like Mapillary Vistas, KITTI, and CamVid. The models are implementations of MobileNetV3 (both large and small variants) with a modified segmentation head based on LR-ASPP. The top model was able to achieve 72.3% mIoU accuracy on Cityscapes val, while running at up to 37.3 FPS on a GPU. Please see below for detailed benchmarks.

image-to-3d-bbox - Build a CNN network to predict 3D bounding box of car from 2D image.

  •    Jupyter

This is an implementation in Keras of the paper "3D Bounding Box Estimation Using Deep Learning and Geometry" (https://arxiv.org/abs/1612.00496).

hart - Hierarchical Attentive Recurrent Tracking

  •    Python

A. R. Kosiorek, A. Bewley, I. Posner, "Hierarchical Attentive Recurrent Tracking", NIPS 2017. The notebook scripts/demo.ipynb contains a demo, which shows how to evaluate tracker on an arbitrary image sequence. By default, it runs on images located in imgs folder and uses a pretrained model. Before running the demo please download AlexNet weights first (described in the Training section).


tfoptflow - Optical Flow Prediction with TensorFlow

  •    Jupyter

This repo provides a TensorFlow-based implementation of the wonderful paper "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume," by Deqing Sun et al. (CVPR 2018). There are already a few attempts at implementing PWC-Net using TensorFlow out there. However, they either use outdated architectures of the paper's CNN networks, only provide TF inference (no TF training), only work on Linux platforms, and do not support multi-GPU training.

Awesome-Autonomous-Driving-Papers - This repository provides awesome research papers for autonomous driving perception

  •    

This repository provides awesome research papers for autonomous driving perception. I have tried my best to keep this repository up to date. If you do find a problem or have any suggestions, please raise this as an issue or make a pull request with information (format of the repo): Research paper title, datasets, metrics, objects, source code, publisher, and year.

RTM3D - Unofficial PyTorch implementation of "RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving" (ECCV 2020)

  •    Python

Generate heatmap for the center and vertexes of objects as the CenterNet paper. If you want to use the strategy from RTM3D paper, you can pass the dynamic-sigma argument to the train.py script. Download the 3D KITTI detection dataset from here.

semantic_kitti_stats - :chart_with_downwards_trend: Get some nice plots with statistics about the Semantic KITTI dataset

  •    Python

This repository holds a script that allows an analysis of the Semantic KITTI Dataset [1,2]. The main focus is on distance and label analysis. For all statistics a csv file and a plot are generated. These instructions will get you a copy of the project up and running on your local machine.

SARosPerceptionKitti - ROS package for the Perception (Sensor Processing, Detection, Tracking and Evaluation) of the KITTI Vision Benchmark Suite

  •    Python

Without assigning any of the abovementioned parameters the demo scenario 0012 is replayed at 20% of its speed with a 3 second delay so RViz has enough time to boot up. If you have any questions, things you would love to add or ideas how to actualize the points in the Area of Improvements, send me an email at simonappel62@gmail.com ! More than interested to collaborate and hear any kind of feedback.

kitti_parser - C++ parser for the RAW KITTI dataset, with callbacks

  •    C++

This is a small helper library that allows for a developer to register callbacks for the different type of measurements in the KITTI dataset. The program is laid out so that the main user interacts with a "Parser" class which handles all the events. In your main method you just need to specify the directory of the KITTI dataset, the methods you want it to callback too, and then run it. This is not multithreaded so it will wait for the method that it calls to finish before it moved on to the next measurement. There are two example main files, please run those to get a feel for how the program interacts. One is just text events, the other displays the stereo images in an OpenCV window.






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