Zobrazeno 1 - 10
of 625
pro vyhledávání: '"Matsushita, Yasuyuki"'
We present a method for 3D reconstruction only using calibrated multi-view surface azimuth maps. Our method, multi-view azimuth stereo, is effective for textureless or specular surfaces, which are difficult for conventional multi-view stereo methods.
Externí odkaz:
http://arxiv.org/abs/2303.16447
This paper presents a near-light photometric stereo method that faithfully preserves sharp depth edges in the 3D reconstruction. Unlike previous methods that rely on finite differentiation for approximating depth partial derivatives and surface norma
Externí odkaz:
http://arxiv.org/abs/2207.04622
We propose a method for estimating high-definition spatially-varying lighting, reflectance, and geometry of a scene from 360$^{\circ}$ stereo images. Our model takes advantage of the 360$^{\circ}$ input to observe the entire scene with geometric deta
Externí odkaz:
http://arxiv.org/abs/2104.09886
This paper proposes a multi-view extension of instance segmentation without relying on texture or shape descriptor matching. Multi-view instance segmentation becomes challenging for scenes with repetitive textures and shapes, e.g., plant leaves, due
Externí odkaz:
http://arxiv.org/abs/2011.13649
This paper addresses the problem of photometric stereo, in both calibrated and uncalibrated scenarios, for non-Lambertian surfaces based on deep learning. We first introduce a fully convolutional deep network for calibrated photometric stereo, which
Externí odkaz:
http://arxiv.org/abs/2007.13145
Akademický článek
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This paper proposes an uncalibrated photometric stereo method for non-Lambertian scenes based on deep learning. Unlike previous approaches that heavily rely on assumptions of specific reflectances and light source distributions, our method is able to
Externí odkaz:
http://arxiv.org/abs/1903.07366
Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision. However, despite the recent advances in generative adversarial networks (GANs), it is still a challenging task to genera
Externí odkaz:
http://arxiv.org/abs/1811.11991
Akademický článek
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This paper describes a method for inferring three-dimensional (3D) plant branch structures that are hidden under leaves from multi-view observations. Unlike previous geometric approaches that heavily rely on the visibility of the branches or use para
Externí odkaz:
http://arxiv.org/abs/1804.09404