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orb_slam2

Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

otavatar

This is the official repository for OTAvatar: One-shot Talking Face Avatar with Controllable Tri-plane Rendering [CVPR2023].

paperfigs

point2mesh

Reconstruct Watertight Meshes from Point Clouds [SIGGRAPH 2020]

pointconv

pointnet

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

pointnet-keras

Keras implementation for Pointnet

pointnet2.scannet

PointNet++ Semantic Segmentation on ScanNet in PyTorch with CUDA acceleration

poissonrecon

Poisson Surface Reconstruction

pspnet

Pyramid Scene Parsing Network, CVPR2017.

pti

Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744

pyslam

pySLAM contains a monocular Visual Odometry (VO) pipeline in Python. It supports many modern local features based on Deep Learning.

pytorch-deeplab-xception

DeepLab v3+ model in PyTorch. Support different backbones.

recent_slam_research

Track Advancement of SLAM 跟踪SLAM前沿动态【ICRA2019已更】

rgbd_ptam

Python implementation of RGBD-PTAM algorithm

romp

ROMP: Monocular, One-stage, Regression of Multiple 3D People, ICCV21

scannet

scenenetrgb-d

Forked from https://bitbucket.org/dysonroboticslab/scenenetrgb-d

scenenn

Supplemental code and scripts for the paper SceneNN: A Scene Meshes Dataset with aNNotations

sdf2sdf

An implementation of the Paper SDF-2-SDF: Highly Accurate 3D Object Reconstruction

semanticfusion

sensor_config

sgan

Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018

shape_as_points

[NeurIPS'21] Shape As Points: A Differentiable Poisson Solver

slslam

Building a 3D Line-based Map Using a Stereo SLAM

social_lstm

social_lstm-1

social_lstm_keras_tf

Social LSTM with Tensorflow (Please see `renew` branch now)

social_lstm_pedestrian_prediction

The aim of the project is to predict the trajectories of pedestrians using lstm neural networks. The project starts from the paper "Social LSTM: Human Trajectory Prediction in Crowded Spaces - Alexandre Alahi, Kratarth Goel, Vignesh Ramanathan, Alexandre Robicquet, Li Fei-Fei, Silvio Savarese - Stanford University - The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 961-971", and its official implementation (https://github.com/vvanirudh/social-lstm-tf) and makes some modifications. This is the Multimedia communication course project made during my final year of the bachelor degree under the supervision of professor Nicola Conci and his phd student Niccolò Bisagno. The modifications introduced are two: - To every simulated pedestrian add the input goal; the goal is the final position (in x and y coordinates) of that pedestrian when it disappears from the video. This modification should improve the predicted trajectory of that pedestrian because of the introduction of this new information - The grid created for every pedestrian in the original project to identify nearby pedestrians is replaced with an array containing the position(in x and y coordinates) of the others pedestrians in distance order, from the closest to the farther. This modification should improve the model results beacuse it presents relevant informations in order to the neural network. Then these two modifications were combined in to a single model. Every model has been evaluated in the test videos with different parameters and in conclusion the model with the two modifications (goal and array) combined performed better than any other model. Also the two modify models performed better than the original model. These results can be seen in the report at page 12 and 13. Unfortunately I haven't the time to translate the report in english, because now is in italian, but the result table at page 12 an 13 should be pretty clear. Technical details: - Programming language: Python 2.7 - Neural networks library used: Tensorflow 1.5 - External libraries: CUDA 8.0, CUDNN 6.0 - OS: Linux, Ubuntu 16.04 distrubution License: GPL v3

spaint

A framework for interactive, real-time 3D scene segmentation