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fudan-zvg/PolarFormer

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PolarFormer

Features

  • 3D Object Detection - Multi-camera 3D detection using polar transformers.

स्टार हिस्ट्री

fudan-zvg/polarformer के लिए स्टार हिस्ट्री चार्टfudan-zvg/polarformer के लिए स्टार हिस्ट्री चार्ट

AI सर्च

और अधिक बेहतरीन रिपॉजिटरी खोजें

अपनी ज़रूरत को सरल भाषा में बताएं — AI हजारों क्यूरेटेड ओपन-सोर्स प्रोजेक्ट्स को प्रासंगिकता के आधार पर रैंक करता है।

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PolarFormer के ओपन-सोर्स विकल्प

समान ओपन-सोर्स प्रोजेक्ट्स, जो PolarFormer के साथ साझा की गई सुविधाओं के आधार पर रैंक किए गए हैं।
  • open-mmlab/mmdetection3dopen-mmlab का अवतार

    open-mmlab/mmdetection3d

    6,273GitHub पर देखें↗

    MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

    Python3d-object-detectionobject-detectionpoint-cloud
    GitHub पर देखें↗6,273
  • open-mmlab/openpcdetopen-mmlab का अवतार

    open-mmlab/OpenPCDet

    5,621GitHub पर देखें↗

    OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud processing framework designed to develop, train, and evaluate machine learning models that identify and locate objects in three dimensional space. The project includes a GPU-accelerated geometry engine for high-performance implementation of 3D intersection over union and rotated non-maximum suppression. It also provides a distributed model training tool to scale the training and testing of detection models across multiple GPUs and computing nodes. The framework covers point c

    Python
    GitHub पर देखें↗5,621
  • manycore-research/spatiallmmanycore-research का अवतार

    manycore-research/SpatialLM

    4,596GitHub पर देखें↗

    SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data into structured indoor semantic maps. It functions as a system for indoor layout estimation and a point cloud semantic parser, converting raw geometric data into representations of architectural elements and object categories. The project aligns multi-modal sensor inputs with linguistic tokens, allowing a language model to serve as a reasoning engine for inferring room topology. It employs mechanisms to convert 3D point clouds and 2D image sequences into discrete tokens and s

    Pythonmllmpoint-cloudsscene-understanding
    GitHub पर देखें↗4,596
  • cliu2/mtransC

    Cliu2/MTrans

    0GitHub पर देखें↗
    GitHub पर देखें↗0
PolarFormer के सभी 30 विकल्प देखें→

अक्सर पूछे जाने वाले प्रश्न

fudan-zvg/polarformer की मुख्य विशेषताएं क्या हैं?

fudan-zvg/polarformer की मुख्य विशेषताएं हैं: 3D Object Detection।

fudan-zvg/polarformer के कुछ ओपन-सोर्स विकल्प क्या हैं?

fudan-zvg/polarformer के ओपन-सोर्स विकल्पों में शामिल हैं: open-mmlab/openpcdet — OpenPCDet is a PyTorch deep learning library and toolbox for LiDAR 3D object detection. It functions as a point cloud… open-mmlab/mmdetection3d — MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting… manycore-research/spatiallm — SpatialLM is a spatial modeling framework that uses large language models to transform monocular video and sensor data… cliu2/mtrans. dvlab-research/uvtr. dscdyc/mssvt.