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of 32
pro vyhledávání: '"Qiu, Jiaxiong"'
3D scene reconstruction is a foundational problem in computer vision. Despite recent advancements in Neural Implicit Representations (NIR), existing methods often lack editability and compositional flexibility, limiting their use in scenarios requiri
Externí odkaz:
http://arxiv.org/abs/2412.02075
Recently, 3D Gaussian Splatting (3DGS) has achieved significant performance on indoor surface reconstruction and open-vocabulary segmentation. This paper presents GLS, a unified framework of surface reconstruction and open-vocabulary segmentation bas
Externí odkaz:
http://arxiv.org/abs/2411.18066
Existing Neural Radiance Fields (NeRF) methods suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field, we propose a multi-space neural radiance field (MS-NeRF)
Externí odkaz:
http://arxiv.org/abs/2305.04268
Neural implicit methods have achieved high-quality 3D object surfaces under slight specular highlights. However, high specular reflections (HSR) often appear in front of target objects when we capture them through glasses. The complex ambiguity in th
Externí odkaz:
http://arxiv.org/abs/2304.08706
The channel redundancy in feature maps of convolutional neural networks (CNNs) results in the large consumption of memories and computational resources. In this work, we design a novel Slim Convolution (SlimConv) module to boost the performance of CN
Externí odkaz:
http://arxiv.org/abs/2003.07469
Outdoor vision robotic systems and autonomous cars suffer from many image-quality issues, particularly haze, defocus blur, and motion blur, which we will define generically as "blindness issues". These blindness issues may seriously affect the perfor
Externí odkaz:
http://arxiv.org/abs/1911.00652
Akademický článek
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Akademický článek
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Autor:
Qiu, Jiaxiong, Cui, Zhaopeng, Zhang, Yinda, Zhang, Xingdi, Liu, Shuaicheng, Zeng, Bing, Pollefeys, Marc
In this paper, we propose a deep learning architecture that produces accurate dense depth for the outdoor scene from a single color image and a sparse depth. Inspired by the indoor depth completion, our network estimates surface normals as the interm
Externí odkaz:
http://arxiv.org/abs/1812.00488
Publikováno v:
Visual Computer; Jun2024, Vol. 40 Issue 6, p4085-4098, 14p
