Search Results for author: Shen Cai

Found 8 papers, 7 papers with code

Unsigned Orthogonal Distance Fields: An Accurate Neural Implicit Representation for Diverse 3D Shapes

1 code implementation3 Mar 2024 Yujie Lu, Long Wan, Nayu Ding, Yulong Wang, Shuhan Shen, Shen Cai, Lin Gao

However, common distance field based implicit representations, specifically signed distance field (SDF) for watertight shapes or unsigned distance field (UDF) for arbitrary shapes, routinely suffer from degradation of reconstruction accuracy when converting to explicit surface points and meshes.

Fast and Interpretable 2D Homography Decomposition: Similarity-Kernel-Similarity and Affine-Core-Affine Transformations

1 code implementation28 Feb 2024 Shen Cai, Zhanhao Wu, Lingxi Guo, Jiachun Wang, Siyu Zhang, Junchi Yan, Shuhan Shen

Under the minimal $4$-point configuration, the first and the last similarity transformations in SKS are computed by two anchor points on target and source planes, respectively.

Computational Efficiency

An Efficient End-to-End 3D Voxel Reconstruction based on Neural Architecture Search

1 code implementation27 Feb 2022 Yongdong Huang, Yuanzhan Li, Xulong Cao, Siyu Zhang, Shen Cai, Ting Lu, Jie Wang, Yuqi Liu

However, many previous works employ neural networks with fixed architecture and size to represent different 3D objects, which lead to excessive network parameters for simple objects and limited reconstruction accuracy for complex objects.

Binary Classification Neural Architecture Search +1

High-fidelity 3D Model Compression based on Key Spheres

1 code implementation19 Jan 2022 Yuanzhan Li, Yuqi Liu, Yujie Lu, Siyu Zhang, Shen Cai, Yanting Zhang

Compared to previous works, our method achieves the high-fidelity and high-compression 3D object coding and reconstruction.

Model Compression Object +1

Spherical Transformer: Adapting Spherical Signal to CNNs

no code implementations11 Jan 2021 Yuqi Liu, Yin Wang, Haikuan Du, Shen Cai

To this end, the proposed method first uses local structured sampling methods such as HEALPix to construct a transformer grid by using the information of spherical points and its adjacent points, and then transforms the spherical signals to the vectors through the grid.

3D Object Classification General Classification +2

InSphereNet: a Concise Representation and Classification Method for 3D Object

1 code implementation25 Dec 2019 Hui Cao, Haikuan Du, Siyu Zhang, Shen Cai

Unlike previous methods that use points, voxels, or multi-view images as inputs of deep neural network (DNN), the proposed method constructs a class of more representative features named infilling spheres from signed distance field (SDF).

3D Object Classification Classification +1

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