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 […]

locate-and-label Code for Two-stage Identifier: “Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition”, accepted at ACL 2021. Setup Requirements conda create –name acl python=3.8 conda activate acl pip install -r requirements.txt Datasets The datasets used in our experiments: Data format: { “tokens”: [“2004-12-20T15:37:00”, “Microscopic”, “microcap”, “Everlast”, “,”, “mainly”, “a”, “maker”, “of”, “boxing”, “equipment”, “,”, “has”, “soared”, “over”, “the”, “last”, “several”, “days”, “thanks”, “to”, “a”, “licensing”, “deal”, “with”, “Jacques”, “Moret”, “allowing”, “Moret”, “to”, “buy”, “out”, “their”, “women”, “‘s”, […]

DFM Python (Pytorch) and Matlab (MatConvNet) implementations of our paper DFM: A Performance Baseline for Deep Feature Matching at CVPR 2021 Image Matching Workshop. Setup Environment We strongly recommend using Anaconda. Open a terminal in ./python folder, and simply run the following lines to create the environment: conda env create -f environment.yml conda activte dfm DependenciesIf you do not use conda, DFM needs the following dependencies:(Versions are not strict; however, we have tried DFM with these specific versions.) python=3.7.1 pytorch=1.7.1 […]

meerkat Meerkat provides fast and flexible data structures for working with complex machine learning datasets. Getting started pip install meerkat-ml Note: some parts of Meerkat rely on optional dependencies. If you know which optional dependencies you’d like to install, you can do so using something like pip install meerkat-ml[dev,text] instead. See setup.py for a full list of optional dependencies. Load your dataset into a DataPanel and get going! import meerkat as mk dp = mk.DataPanel.from_csv(“…”) What is Meerkat? Meerkat makes […]

Who Left the Dogs Out? Evaluation and demo code for our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop. Install Clone the repository with submodules: git clone –recurse-submoduleshttps://github.com/benjiebob/WLDO For segmentation decoding, install pycocotools python -m pip install “git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI” Datasets To use the StanfordExtra dataset, you will need to download the .json file via the repository. Please ensure you have StanfordExtra_v12 installed, which we released 1 Feb 2021. You may also wish […]

U Net Lowered with Keras Complete U-net Implementation with keras The model is implemented using the original paper. But I have changed the number of filters of the layers. The implemented number of layers are reduced to 25% of the original paper. Original Model Architecture : Dataset : The dataset has been taken from kaggle . It had a specific directory tree, but it was tough to execute dataset building from it, so I prepared an usable dat directory. Link […]

RGBT Crowd Counting Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting” Lingbo Liu, Jiaqi Chen, Hefeng Wu, Guanbin Li, Chenglong Li, Liang Lin. “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting.” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [PDF] Download RGBT-CC Dataset & Models: [Dropbox][BaiduYun (PW: RGBT)] Our framework can be implemented with various backbone networks. You can refer to this page for […]

DANNet DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation Requirements python3.7 pytorch==1.5.0 cuda10.2 Datasets Cityscapes: Please follow the instructions in Cityscape to download the training set. Dark-Zurich: Please follow the instructions in Dark-Zurich to download the training/val/test set. Testing If needed, please directly download the visualization results of our method for Dark-zurich-val and Dark-zurich-test. To reproduce the reported results in our paper (on Dark-Zurich val), please follow these steps: Step1: download the [trained models](https://www.dropbox.com/s/fmlq806p2wqf311/trained_models.zip?dl=0) and put it […]