Semantic Segmentation
Showing 36 of 43 projects
A graphical image annotation tool written in Python for computer vision tasks like segmentation and detection.
A PyTorch library providing 12+ semantic segmentation model architectures with 800+ pretrained convolutional and transformer-based encoders.
A curated list of semantic segmentation papers, code, datasets, and resources across various deep learning frameworks.
A hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras with modern features, loop closure, and dense reconstruction.
An open source Python library and framework for building computer vision models on satellite, aerial, and large imagery sets.
A C++ library for real-time metric-semantic SLAM, building semantically annotated 3D meshes from camera and IMU data.
An end-to-end Python pipeline for semantic segmentation of aerial and satellite imagery to extract features like buildings and roads.
A web-based labeling tool for creating semantic segmentation training data from 2D images and 3D point clouds.
An end-to-end deep learning system for reconstructing complete 3D scenes (geometry and semantics) from posed 2D images.
An efficient neural network for semantic segmentation of large-scale 3D point clouds using random sampling.
A fast, modular PyTorch reference implementation for training and evaluating semantic segmentation models.
A PyTorch framework for efficient 3D semantic and panoptic segmentation using superpoint-based transformer architectures.
A framework for semantic and instance segmentation of LiDAR point clouds using range images, designed for autonomous driving applications.
An efficient LiDAR-based semantic SLAM system that builds 3D semantic maps from laser scans.
Deep learning inference nodes for ROS/ROS2 with support for NVIDIA Jetson devices and TensorRT.
A PyTorch framework for semantic segmentation of large 3D point clouds using superpoint graphs.
A TensorFlow implementation for generating semantically segmented bird's eye view images from multiple vehicle-mounted cameras using a Sim2Real deep learning approach.
A desktop tool for labeling individual points and polygons in LiDAR point cloud datasets, specifically designed for KITTI format.
Real-time 3D semantic reconstruction library for robotics, building dense metric-semantic maps from 2D sensor data.
Real-time 3D semantic mapping system using a handheld RGB-D camera, built on ROS with ORB_SLAM2 and PSPNet.
A learning-based approach for moving object segmentation in 3D LiDAR data, distinguishing moving vs. static objects in real-time.
A curated list of popular deep learning models for image classification, segmentation, and detection with key performance metrics.
A lightweight encoder-decoder neural network for real-time semantic segmentation on resource-constrained devices.
A real-time, uncertainty-aware deep learning model for semantic segmentation of 3D LiDAR point clouds in autonomous driving.
Utility scripts for loading, visualizing, and inspecting the KITTI-360 autonomous driving dataset.
A lightweight neural network for near-real-time semantic segmentation of LiDAR point clouds using polar coordinate quantization.
A centralized Python framework for agricultural machine learning, providing access to public datasets, benchmarks, pretrained models, and synthetic data generation.
A Python devkit for loading, exploring, and manipulating the PandaSet, a large-scale autonomous driving dataset with LiDAR, camera, and annotations.
A PyTorch framework for deep learning on point clouds, providing a modular and reproducible foundation for 3D vision tasks.
ROS package for sensor processing, object detection, tracking, and evaluation using the KITTI Vision Benchmark dataset.
A PyTorch-based Python package for deep and machine learning analysis of microscopy data, designed for domain scientists.
Automatically classifies and labels urban point clouds using data fusion with public datasets and region growing techniques.
An open-source benchmark solution for the Kaggle TGS Salt Identification Challenge using semantic segmentation.
A collection of Google Colab tutorials teaching biologists how to apply deep learning with Keras to real-world biological and agricultural problems.
A city-scale dataset and platform for learning holistic 3D structures from panoramic and perspective imagery with detailed annotations.
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A pre-release Caffe branch for fully convolutional networks (FCNs), now deprecated with features merged into Caffe master.
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