A PyTorch implementation of NeRF (Neural Radiance Fields) that reproduces the results.

Monocular Depth Prediction

Train neural network for semantic segmentation (deep lab V3) with pytorch in less then 50 lines of code

Unofficial implementation of Swin-Transformer-Semantic-Segmentation, relatively independent code, easy to add to other models.

Visuals contains figures and templates which you can reuse and customize to improve your scientific writing.

mIOU=80.02 on cityscapes. My implementation of deeplabv3+ (also know as 'Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation' based on the dataset of cityscapes).

Simultaneous Visual Odometry, Object Detection, and Instance Segmentation

CubeSLAM: Monocular 3D Object Detection and SLAM

Multi-Task (Joint Segmentation / Depth / Surface Normas) Real-Time Light-Weight RefineNet

Convolutional Spatial Propagation Network

Pytorch implementation for Semantic Segmentation/Scene Parsing on MIT ADE20K dataset

DeepLab v3+ model in PyTorch. Support different backbones.

Semantic Segmentation Suite in TensorFlow. Implement, train, and test new Semantic Segmentation models easily!

Here is a pytorch implementation of deeplabv3+ supporting ResNet(79.155%) and Xception(79.945%). Multi-scale & flip test and COCO dataset interface has been finished.

Real-time dense visual SLAM system

Source code (train/test) accompanying the paper entitled "Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach"…

orb-slam2 with semantic label

PyTorch implementation of Convolutional Spatial Propagation Network

Real time semantic slam in ROS with a hand held RGB-D camera

Basic algorithms for lidar slam.

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.