Code for paper: Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo (CVPR 2020 Oral)

Code release for NeuS

Code for BrnoCompSpeed dataset

Official code of PatchmatchNet (CVPR 2021 Oral)

Code for 3D Reconstruction of Novel Object Shapes from Single Images paper

Joint Detection and Embedding for fast multi-object tracking

Learning to Cluster Faces (CVPR 2019, CVPR 2020)

Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.

[IJCV-2021] FairMOT: On the Fairness of Detection and Re-Identification in Multi-Object Tracking

A deep learning based 3D reconstruction project with MVSNet for depth infering.

Project page of paper "Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoning"

Code release of paper "Deep Multi-View Stereo gone wild"

Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images. In ECCV2018.

We evaluate our method on different datasets (including ShapeNet, CUB-200-2011, and Pascal3D+) and achieve state-of-the-art results, outperforming all the other supervised and unsupervised methods …

(latest updates and bug fixed) DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Code for "NeRS: Neural Reflectance Surfaces for Sparse-View 3D Reconstruction in the Wild," in NeurIPS 2021

🏆The 1st place solution of track3 (City-Scale Multi-Camera Vehicle Tracking) in the NVIDIA AI City Challenge at CVPR 2021 Workshop.

Code for "NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video", CVPR 2021 oral

Code release for Local Light Field Fusion at SIGGRAPH 2019

Code release for NeRF (Neural Radiance Fields)

Visibility-aware Multi-view Stereo Network

C++ associative containers

A challenging, in the wild dataset for experimentation with face masks with 63,072 face images.

Multi-view unsynchronized 3D trajectory reconstruction

Learning Generative Models of Textured 3D Meshes from Real-World Images, ICCV 2021

Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2020"