clDice CVPR 2021 Authors: Suprosanna Shit and Johannes C. Paetzold et al. @article{shit2020cldice, title={clDice – a Topology-Preserving Loss Function for Tubular Structure Segmentation}, author={Shit, Suprosanna and Paetzold, Johannes C and Sekuboyina, Anjany and Zhylka, Andrey and Ezhov, Ivan and Unger, Alexander and Pluim, Josien PW and Tetteh, Giles and Menze, Bjoern H}, journal={arXiv preprint arXiv:2003.07311}, year={2020} } Abstract Accurate segmentation of tubular, network-like structures, such as vessels, neurons, or roads, is relevant to many fields of research. For such structures, […]

StyleSpace-pytorch Implementation of StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation (https://arxiv.org/pdf/2011.12799.pdf) in PyTorch This implementation is mostly relied on rosinality’s stylegan2-pytorch Requirements I have tested on: Usage For the index and channel, please check the paper (https://arxiv.org/pdf/2011.12799.pdf), e.g., (11_286), channel 286 of generator level 11. FFHQ Firstly, you should download pretrained model from here and place the stylegan2-ffhq-config-f.pkl into pretrained folder. Open the notebook StyleSpace_FFHQ.ipynb Car LSUN GitHub https://github.com/xrenaa/StyleSpace-pytorch    

PointCloudDeNoising Point Cloud Denoising Abstract Lidar sensors are frequently used in environment perception for autonomous vehicles and mobile robotics to complement camera, radar, and ultrasonic sensors. Adverse weather conditions are significantly impacting the performance of lidar-based scene understanding by causing undesired measurement points that in turn effect missing detections and false positives.In heavy rain or dense fog, water drops could be misinterpreted as objects in front of the vehicle which brings a mobile robot to a full stop.In this paper, […]

FastPitchFormant – PyTorch Implementation PyTorch Implementation of FastPitchFormant: Source-filter based Decomposed Modeling for Speech Synthesis. Dependencies You can install the Python dependencies with pip3 install -r requirements.txt Inference You have to download the pretrained models and put them in output/ckpt/LJSpeech/. For English single-speaker TTS, run python3 synthesize.py –text “YOUR_DESIRED_TEXT” –restore_step 1000000 –mode single -p config/LJSpeech/preprocess.yaml -m config/LJSpeech/model.yaml -t config/LJSpeech/train.yaml The generated utterances will be put in output/result/. Batch Inference Batch inference is also supported, try python3 synthesize.py –source preprocessed_data/LJSpeech/val.txt –restore_step […]

robust_cvd This repository contains Python and C++ implementation of Robust Consistent Video Depth, as described in the paper Johannes Kopf, Xuejian Rong, and Jia-Bin Huang. Robust Consistent Video Despth Estimation. CVPR 2021 Project | Paper | Video | Colab We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate […]