Search Results for author:
Found 10 papers, 7 papers with code
C3DPO: Canonical 3D Pose Networks for Non-Rigid Structure From Motion
We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images.
Accelerating 3D Deep Learning with PyTorch3D
We address these challenges by introducing PyTorch3D, a library of modular, efficient, and differentiable operators for 3D deep learning.
Worldsheet: Wrapping the World in a 3D Sheet for View Synthesis from a Single Image
We present Worldsheet, a method for novel view synthesis using just a single RGB image as input.
PyTorchVideo: A Deep Learning Library for Video Understanding
We introduce PyTorchVideo, an open-source deep-learning library that provides a rich set of modular, efficient, and reproducible components for a variety of video understanding tasks, including classification, detection, self-supervised learning, and low-level processing.
Recognizing Scenes from Novel Viewpoints
Humans can perceive scenes in 3D from a handful of 2D views.
Omnivore: A Single Model for Many Visual Modalities
Prior work has studied different visual modalities in isolation and developed separate architectures for recognition of images, videos, and 3D data.
Ranked #1 on
Scene Recognition
on SUN-RGBD
(using extra training data)
Learning 3D Object Shape and Layout without 3D Supervision
A 3D scene consists of a set of objects, each with a shape and a layout giving their position in space.
Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild
In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e. g. urban driving scenes.
3D Object Detection
3D Object Detection From Monocular Images
+2
Segment Anything
We introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation.
Ranked #2 on
Zero-Shot Instance Segmentation
on LVIS v1.0 val
FACET: Fairness in Computer Vision Evaluation Benchmark
We present a new benchmark named FACET (FAirness in Computer Vision EvaluaTion), a large, publicly available evaluation set of 32k images for some of the most common vision tasks - image classification, object detection and segmentation.
