Showing 4 open source projects for "objects detection"

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  • 1
    Mesh R-CNN

    Mesh R-CNN

    code for Mesh R-CNN, ICCV 2019

    ...Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh R-CNN uses a differentiable mesh representation, allowing it to efficiently refine surface geometry while maintaining high spatial detail. The system combines 2D detection from Mask R-CNN with 3D reasoning modules that output full mesh reconstructions aligned with the input image. ...
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  • 2
    Objectron

    Objectron

    A dataset of short, object-centric video clips

    ...The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras.
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  • 3
    Affine Transformation of Virtual Object

    Affine Transformation of Virtual Object

    Transformation virtual 3D object using a finger gesture-based system

    ...This is the version 2.0 that includes a more generalized affine transformation of virtual objects in the virtual environment with more experimentation and analysis. Previous versions only include the geometric transformation of a virtual 3D object with respect to a finger gesture. Paper for the affine transformation of the virtual 3D object has been published in Virtual Reality & Intelligent Hardware, Elsevier Science Publishers in 2020.
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  • 4
    House3D

    House3D

    A Realistic and Rich 3D Environment

    House3D is a large-scale virtual 3D simulation environment designed to support research in embodied AI, reinforcement learning, and vision-language navigation. It provides more than 45,000 richly annotated indoor scenes sourced from the SUNCG dataset, covering diverse architectural layouts such as studios, multi-floor homes, and spaces with detailed furnishings and room types. Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings...
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