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swayfreeda's Projects

3dunetcnn icon 3dunetcnn

Keras 3D U-Net Convolution Neural Network (CNN) designed for medical image segmentation

a-loam icon a-loam

Advanced implementation of LOAM

activitynet icon activitynet

2016 ActivityNet action recognition challenge. CNN + LSTM approach. Multi-threaded loading.

adversarial icon adversarial

Code and hyperparameters for the paper "Generative Adversarial Networks"

ants icon ants

Advanced Normalization Tools (ANTs)

approxmvbb icon approxmvbb

Fast algorithms to compute an approximation of the minimal volume oriented bounding box of a point cloud in 3D.

bundlefusion icon bundlefusion

BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

caffe icon caffe

Caffe: a fast open framework for deep learning.

caffe-segnet icon caffe-segnet

Implementation of SegNet: A Deep Convolutional Encoder-Decoder Architecture for Semantic Pixel-Wise Labelling

cartographer icon cartographer

Cartographer is a system that provides real-time simultaneous localization and mapping (SLAM) in 2D and 3D across multiple platforms and sensor configurations.

carve icon carve

Carve is a fast, robust constructive solid geometry library. (fork from https://code.google.com/p/carve/)

centernet icon centernet

Object detection, 3D detection, and pose estimation using center point detection:

cgal icon cgal

The public CGAL repository

cmvs-pmvs icon cmvs-pmvs

This software (CMVS) takes the output of a structure-from-motion (SfM) software as input, then decomposes the input images into a set of image clusters of managable size. An MVS software can be used to process each cluster independently and in parallel, where the union of reconstructions from all the clusters should not miss any details that can be otherwise obtained from the whole image set. CMVS should be used in conjunction with an SfM software Bundler and an MVS software PMVS2 (PMVS version 2).

cnn-dso icon cnn-dso

Direct Sparse Odometry with CNN Depth Prediction

code icon code

Code for the book "Mastering OpenCV with Practical Computer Vision Projects" by Packt Publishing 2012.

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