There are 1 repository under svo topic.
A GPU SVO Builder using rasterization pipeline, a efficient SVO ray marcher and a simple SVO path tracer.
This repository intends to enable autonomous drone delivery with the Intel Aero RTF drone and PX4 autopilot. The code can be executed both on the real drone or simulated on a PC using Gazebo. Its core is a robot operating system (ROS) node, which communicates with the PX4 autopilot through mavros. It uses SVO 2.0 for visual odometry, WhyCon for visual marker localization and Ewok for trajectoy planning with collision avoidance.
Hardware accelerated voxel ray marching
Official implementation and dataset for the NAACL 2024 paper "ComCLIP: Training-Free Compositional Image and Text Matching"
Mainstream SLAM framework source code analysis
BlackPearl Rendering Engine
Java/LWJGL pathtracer based on the paper "Efficient Sparse Voxel Octrees" by Samuli Laine and Tero Karras.
A SVO based SLAM implementation for stereo cameras
Voxel rendering engine for master thesis (voxel skeletal animation)
Automatic SVO Extraction Tool for Social Science
The web contains vast repositories of unstructured text. We investigate the opportunity for building a knowledge graph from these text sources. We generate a set of triples which can be used in knowledge gathering and integration. We define the architecture of a language compiler for processing subject-predicate-object triples using the OpenNLP parser. We implement a depth-first search traversal on the POS tagged syntactic tree appending predicate and object information. A parser enables higher precision and higher recall extractions of syntactic relationships across conjunction boundaries. We are able to extract 2-2.5 times the correct extractions of ReVerb. The extractions are used in a variety of semantic web applications and question answering. We verify extraction of 50,000 triples on the ClueWeb dataset.
Subject-verb-object triplets extraction for russian language.
Compute the distance between people using a Stereolabs ZED stereovision camera or SVO file. People detection is done by YOLOv3 with a Tensorflow backend. Tracking is done by DeepSORT.