abcSup/NotEnoughSleepAI
Implementation of the Multi-Task Multi-Sensor Fusion for 3D Object Detection paper by Uber
This project helps self-driving car engineers and researchers build systems that can detect and locate objects in 3D space. It takes in raw data from both LiDAR sensors (point clouds) and cameras (images) and outputs precise 3D bounding boxes around detected objects. This is crucial for autonomous vehicles to understand their surroundings.
No commits in the last 6 months.
Use this if you are developing or researching perception systems for autonomous vehicles and need to accurately identify and pinpoint objects in 3D using a combination of sensor data.
Not ideal if you are looking for a system to detect objects in 2D images only or if your application does not involve LiDAR data.
Stars
93
Forks
23
Language
Jupyter Notebook
License
—
Category
Last pushed
Sep 30, 2020
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/abcSup/NotEnoughSleepAI"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DeepTrackAI/DeepTrack2
DeepTrack2 is a modular Python library for generating, manipulating, and analyzing image data...
abhineet123/Deep-Learning-for-Tracking-and-Detection
Collection of papers, datasets, code and other resources for object tracking and detection using...
NVIDIA-ISAAC-ROS/isaac_ros_dnn_inference
NVIDIA-accelerated DNN model inference ROS 2 packages using NVIDIA Triton/TensorRT for both...
DagnyT/hardnet
Hardnet descriptor model - "Working hard to know your neighbor's margins: Local descriptor...
rafellerc/Pytorch-SiamFC
Pytorch implementation of "Fully-Convolutional Siamese Networks for Object Tracking"