Popular GitHub Repositories for Cm360

[CVPR 2022] "MonoScene: Monocular 3D Semantic Scene Completion": 3D Semantic Occupancy Prediction from a single image

? WorldGen - Generate Any 3D World in Second

Collective Knowledge (CK), Collective Mind (CM) and Common Metadata eXchange (CMX): community-driven projects to facilitate collaborative and reproducible research and to learn how to run AI, ML, and other emerging workloads more efficiently and cost-effectively across diverse models, datasets, software, and hardware using MLPerf.

Code accompanying the paper "Spherical View Synthesis for Self-Supervised 360 Depth Estimation", 3DV 2019

[CVPR 2024] Official PyTorch Code of SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects

Code and models for "Pano3D: A Holistic Benchmark and a Solid Baseline for 360 Depth Estimation", OmniCV Workshop @ CVPR21.

[CVPR 2024] ?Know Your Neighbors: Improving Single-View Reconstruction via Spatial Vision-Language Reasoning

BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Deep Lighting Environment Map Estimation from Spherical Panoramas (CVPRW20)

CM-NAS: Cross-Modality Neural Architecture Search for Visible-Infrared Person Re-Identification (ICCV2021)

Master's project implementing depth estimation for spherical images using unsupervised learning with CNNs.

[NeurIPS2023] PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas(or 360-degree image)

Code and models for "PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes" presented at the OmniCV workshop of CVPR21.

This repository contains python implementation of the paper: Super-resolution of Omnidirectional Images Using Adversarial Learning

[ECCV 2024] PanoFree: Tuning-Free Holistic Multi-view Image Generation with Cross-view Self-Guidance

we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task.

Responsible AI Masterclass (June 2024 Run)

This notebook is ispired by the AIX360 HELOC Credit Approval Tutorial, which shows different explainability methods for a credit approval process. Here XGBoost is used for classification, achieving better accuracy than most of the models used in that notebook. Then, feature importance methods are shown, to be compared with the Data Scientist explanations methods provided in the above notebook. The first ones come directly with XGBoost and the other is based on SHAP.

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.