Latent Partition Implicit with Surface Codes for 3D Representation

Chen, Chao; Liu, Yu‐Shen; Han, Zhizhong

doi:10.1007/978-3-031-20062-5_19

Cited by 12 publications

(2 citation statements)

References 80 publications

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“…To resolve this, several works explored capturing local features both in the 2D image field (Saito et al 2019(Saito et al , 2020Xu et al 2019) and in the 3D point cloud field (Chibane, Alldieck, and Pons-Moll 2020;Peng et al 2020;Chen, Liu, and Han 2022;Baorui et al 2022).…”

Section: Related Work 3d Representationsmentioning

confidence: 99%

GridFormer: Point-Grid Transformer for Surface Reconstruction

Li,

Gao,

Liu

et al. 2024

AAAI

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Implicit neural networks have emerged as a crucial technology in 3D surface reconstruction. To reconstruct continuous surfaces from discrete point clouds, encoding the input points into regular grid features (plane or volume) has been commonly employed in existing approaches. However, these methods typically use the grid as an index for uniformly scattering point features. Compared with the irregular point features, the regular grid features may sacrifice some reconstruction details but improve efficiency. To take full advantage of these two types of features, we introduce a novel and high-efficiency attention mechanism between the grid and point features named Point-Grid Transformer (GridFormer). This mechanism treats the grid as a transfer point connecting the space and point cloud. Our method maximizes the spatial expressiveness of grid features and maintains computational efficiency. Furthermore, optimizing predictions over the entire space could potentially result in blurred boundaries. To address this issue, we further propose a boundary optimization strategy incorporating margin binary cross-entropy loss and boundary sampling. This approach enables us to achieve a more precise representation of the object structure. Our experiments validate that our method is effective and outperforms the state-of-the-art approaches under widely used benchmarks by producing more precise geometry reconstructions. The code is available at https://github.com/list17/GridFormer.

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Section: Related Work 3d Representationsmentioning

confidence: 99%

GridFormer: Point-Grid Transformer for Surface Reconstruction

Li,

Gao,

Liu

et al. 2024

AAAI

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“…Points2surf [19] used autoencoders to obtain more accurate distance values from local sources and more accurate sign values from global sources. The combination of the two has both local and global information, which can quickly and conveniently obtain sign distance values and improve reconstruction efficiency; latent partition implicit (LPI) [20] combined local regions into global shapes in implicit space, and the use of affinity vectors allowed the reconstruction results to include both local region features and cleverly integrate global information, resulting in currently outstanding reconstruction results; implicit functions in reconstruction and completion (IFRC) [21] proposed an implicit feature network that does not use a single vector to encode three-dimensional shapes. Instead, it extracts a learnable deep feature tensor for three-dimensional multi-scale deep features and aligns it with the original Euclidean space of the embedded shape, allowing the model to make decisions based on global and local shape structures; Octfield [22] proposed adaptive decomposition in 3D scenes, which only distributes local implicit functions around the surface of interest, connects the shape features of different layers, and possesses both local and global information, achieving excellent reconstruction accuracy.…”

Section: Related Workmentioning

confidence: 99%

3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function

Li,

Su,

Jiang

et al. 2024

Algorithms

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Three-dimensional reconstruction from point clouds is an important research topic in computer vision and computer graphics. However, the discrete nature, sparsity, and noise of the original point cloud contribute to the results of 3D surface generation based on global features often appearing jagged and lacking details, making it difficult to describe shape details accurately. We address the challenge of generating smooth and detailed 3D surfaces from point clouds. We propose an adaptive octree partitioning method to divide the global shape into local regions of different scales. An iterative loop method based on GRU is then used to extract features from local voxels and learn local smoothness and global shape priors. Finally, a moving least-squares approach is employed to generate the 3D surface. Experiments demonstrate that our method outperforms existing methods on benchmark datasets (ShapeNet dataset, ABC dataset, and Famous dataset). Ablation studies confirm the effectiveness of the adaptive octree partitioning and GRU modules.

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Multi-grid representation with field regularization for self-supervised surface reconstruction from point clouds

Jin

Zhou

2023

Computers & Graphics

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Latent Partition Implicit with Surface Codes for 3D Representation

Cited by 12 publications

References 80 publications

GridFormer: Point-Grid Transformer for Surface Reconstruction

GridFormer: Point-Grid Transformer for Surface Reconstruction

3D Reconstruction Based on Iterative Optimization of Moving Least-Squares Function

Multi-grid representation with field regularization for self-supervised surface reconstruction from point clouds

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