Multi View Neural Surface Reconstruction With Structured Light To overcome this problem, we introduce active sensing with structured light (sl) into multi view 3d object reconstruction based on dr to learn the unknown geometry and appearance of arbitrary scenes and camera poses. We aim to reconstruct the 3d shape of an object from multi view structured light (sl) pat tern images with rough camera poses with known intrinsic parameters. additionally, the proposed method does not require mask supervision.
Multi View Neural Surface Reconstruction With Structured Light To overcome this problem, we introduce active sensing with structured light (sl) into multi view 3d object reconstruction based on dr to learn the unknown geometry and appearance of. Contribute to pfnet research neussl development by creating an account on github. I received my m.eng., and d.eng. from tokyo tech in 2019 and 2022. during this period, my supervisor was professor masatoshi okutomi. i also worked with assistant professor akihiko torii and. We trained our network on real world 2d images of objects with different material properties, lighting conditions, and noisy camera initializations from the dtu mvs dataset. we found our model to produce state of the art 3d surface reconstructions with high fidelity, resolution and detail.
Github Zhangyl34 Multiview Neural Surface Reconstruction I received my m.eng., and d.eng. from tokyo tech in 2019 and 2022. during this period, my supervisor was professor masatoshi okutomi. i also worked with assistant professor akihiko torii and. We trained our network on real world 2d images of objects with different material properties, lighting conditions, and noisy camera initializations from the dtu mvs dataset. we found our model to produce state of the art 3d surface reconstructions with high fidelity, resolution and detail. In this work we address the challenging problem of multiview 3d surface reconstruction. we introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. To overcome this problem, we introduce active sensing with structured light (sl) into multi view 3d object reconstruction based on dr to learn the unknown geometry and appearance of arbitrary scenes and camera poses. To make mvps more accessible, we introduce a practical and easy to implement setup, multi view constrained photometric stereo (mvcps), where the light directions are unknown but con strained to move together with the camera.
Lior Yariv Yoni Kasten Dror Moran Meirav Galun Matan Atzmon Ronen In this work we address the challenging problem of multiview 3d surface reconstruction. we introduce a neural network architecture that simultaneously learns the unknown geometry, camera parameters, and a neural renderer that approximates the light reflected from the surface towards the camera. To overcome this problem, we introduce active sensing with structured light (sl) into multi view 3d object reconstruction based on dr to learn the unknown geometry and appearance of arbitrary scenes and camera poses. To make mvps more accessible, we introduce a practical and easy to implement setup, multi view constrained photometric stereo (mvcps), where the light directions are unknown but con strained to move together with the camera.
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