Search Results for "maps"

VGGT-Omega is a Facebook Research computer vision project for feed-forward camera and depth reconstruction. It takes images as input and predicts camera parameters, depth maps, confidence values, and related scene tokens. The project is associated with 3D understanding workflows where models infer scene geometry without a traditional multi-stage reconstruction pipeline. It includes pretrained model variants with different resolutions and text-alignment capabilities, though checkpoint access may require approval. ...

...Built on top of plotly.js, it is a high-level, declarative charting library that ships with more than 30 chart types. Everything from statistical charts and scientific charts, through to maps, 3D graphs and animations, plotly.py lets you create them all. Graphs made with plotly.py can be viewed in Jupyter notebooks, standalone HTML files, or hosted online using Chart Studio Cloud.

...Each of the thousands of sequences in CO3Dv2 captures a common object (from categories like cars, chairs, or plants) from multiple real-world viewpoints. The dataset includes RGB images, depth maps, masks, and camera poses for each frame, along with pre-defined training, validation, and testing splits for both few-view and many-view reconstruction tasks.

Django + shell_plus + Jupyter notebooks made easy. A Jupyter notebook with access to objects from the Django ORM is a powerful tool to introspect data and run ad-hoc queries. Built-in integration with the imported objects from django-extensions shell_plus. Saves the state between sessions so you don't need to remember what you did. Inheritance diagrams on any object, including ORM models.

A Python package for integrating CARTO maps, analysis, and data services into data science workflows. Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO's services like hosted, dynamic or static maps and Data Observatory augmentation.

Nerfies demonstrates deformation-aware neural radiance fields that reconstruct and render dynamic, real-world scenes from casual video. Instead of assuming a static world, the method learns a canonical space plus a deformation field that maps changing poses or expressions back to that space during training. This lets the system generate photorealistic novel views of nonrigid subjects—faces, bodies, cloth—while preserving fine detail and consistent lighting. The training pipeline handles imperfect captures by modeling camera poses, exposure variations, and background segmentation, producing stable geometry and appearance. ...