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20 search results found
Py360convert
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283
Python implementation of convertion between equirectangular, cubemap and perspective. (equirect2cube, cube2equirect, equirect2perspec)
Sphericalviewsynthesis
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83
Code accompanying the paper "Spherical View Synthesis for Self-Supervised 360 Depth Estimation", 3DV 2019
3d60
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70
Tools accompanying the 3D60 spherical panoramas dataset
360sd Net
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68
Pytorch implementation of ICRA 2020 paper "360° Stereo Depth Estimation with Learnable Cost Volume"
360sr Challenge
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61
NTIRE 2023 Challenge on 360° Omnidirectional Image and Video Super-Resolution
Deeppanoramalighting
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51
Deep Lighting Environment Map Estimation from Spherical Panoramas (CVPRW20)
Bifusev2
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41
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation
Monodepth360
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39
Master's project implementing depth estimation for spherical images using unsupervised learning with CNNs.
Tangent Images
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39
Official repository for "Tangent Images for Mitigating Spherical Distortion," CVPR 2020
Direct_360_fpe
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38
[RA-L'22] 360-DFPE: Leveraging Monocular 360-Layouts for Direct Floor Plan Estimation
360qvm_bypass
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27
通过生成不同hash的ico并写入程序中,实现批量bypass360QVM
Pysphere
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25
control Mi Sphere 360 camera from command line
Panodr
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16
Code and models for "PanoDR: Spherical Panorama Diminished Reality for Indoor Scenes" presented at the OmniCV workshop of CVPR21.
Panogrf
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16
[NeurIPS2023] PanoGRF: Generalizable Spherical Radiance Fields for Wide-baseline Panoramas(or 360-degree image)
Threesixty
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15
360-degree feedback tool
Hyperspheresurfaceregression
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13
Code accompanying the paper "360 Surface Regression with a Hyper-Sphere Loss", 3DV 2019
Singleshotcuboids
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12
Code accompanying the paper "Single-Shot Cuboids: Geodesics-based End-to-end Manhattan Aligned Layout Estimation from Spherical Panoramas".
Qgisglviewer
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10
A simple streetview-like Qgis plugin for equirectangular image visualisation
360fy 360_video_creator
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8
A 360° video creator built using simpleCV, Python 2.7 and Qt
360 Mlc
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6
[NeurIPS'22] 360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning
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