POCO: Point Convolution for Surface Reconstruction

Boulch, Alexandre; Marlet, Renaud

doi:10.48550/arxiv.2201.01831

Cited by 2 publications

(6 citation statements)

References 92 publications

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“…In order to verify the superiority of the proposed GONet, we quantitatively compare GONet with six state-of-the-art methods in point cloud surface reconstruction task, i.e., PSGN [6], DMC [20], OccNet [5], POCO [7], ConvONet [8], and SAConvONet [36]. For point cloud inputs, we adapt PSGN by changing the encoder to pointnet.…”

Section: Reconstruction Resultsmentioning

confidence: 99%

“…SA-ConvONet [36] proposes sign-agnostic optimization on the basis of ConvONet and modifies the structure of ConvONet into 3D U-Net. To preserve and utilize the direct connection with the input points, POCO [7] uses point convolutions and computes latent features at each input point.…”

Section: Related Workmentioning

confidence: 99%

“…We randomly sample 1024 points on each satellite mesh as the input point cloud. We implement PSGN [6] https://github.com/fanhqme/PointSetGeneration (accessed on 1 June 2023), DMC [20] (https://avg.is.mpg.de/research_projects/deepmarching-cubes) (accessed on 1 June 2023), OccNet [5] (https://github.com/LMescheder/ Occupancy-Networks) (accessed on 1 June 2023), POCO [7] (https://github.com/valeoai/ POCO) (accessed on 1 June 2023), ConvONet [8] (https://github.com/autonomousvision/ convolutional_occupancy_networks) (accessed on 1 June 2023), and SAConvONet [36] (https://github.com/tangjiapeng/SA-ConvONet) (accessed on 1 June 2023) by adopting their official open source. We evaluate all the models using two NVIDIA TITAN 24 GB GPUs and a Intel i9-10900X CPU.…”

Section: Implementation Detailsmentioning

confidence: 99%

“…These methods perform well in mesh reconstruction from the sparse point cloud through fitting complex mapping functions from the sparse point cloud to its surface. However, due to the lack of satellite datasets, all reconstruction methods [4][5][6][7][8][9][10][11][12] focus on common objects, such as tables, cars, sofas, etc. Our work hopes to break through the problem of satellite mesh reconstruction from sparse point clouds.…”

Section: Introductionmentioning

confidence: 99%

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Learning Implicit Neural Representation for Satellite Object Mesh Reconstruction

Yang,

Cao,

et al. 2023

Remote Sensing

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Constructing a surface representation from the sparse point cloud of a satellite is an important task for satellite on-orbit services such as satellite docking and maintenance. In related studies on surface reconstruction from point clouds, implicit neural representations have gained popularity in learning-based 3D object reconstruction. When aiming for a satellite with a more complicated geometry and larger intra-class variance, existing implicit approaches cannot perform well. To solve the above contradictions and make effective use of implicit neural representations, we built a NASA3D dataset containing point clouds, watertight meshes, occupancy values, and corresponding points by using the 3D models on NASA’s official website. On the basis of NASA3D, we propose a novel network called GONet for a more detailed reconstruction of satellite grids. By designing an explicit-related implicit neural representation of the Grid Occupancy Field (GOF) and introducing it into GONet, we compensate for the lack of explicit supervision in existing point cloud surface reconstruction approaches. The GOF, together with the occupancy field (OF), serves as the supervised information for neural network learning. Learning the GOF strengthens GONet’s attention to the critical points of the surface extraction algorithm Marching Cubes; thus, it helps improve the reconstructed surface’s accuracy. In addition, GONet uses the same encoder and decoder as ConvONet but designs a novel Adaptive Feature Aggregation (AFA) module to achieve an adaptive fusion of planar and volume features. The insertion of AFA allows for the obtained implicit features to incorporate more geometric and volumetric information. Both visualization and quantitative experimental results demonstrate that our GONet could handle 3D satellite reconstruction work and outperform existing state-of-the-art methods by a significant margin. With a watertight mesh, our GONet achieves 5.507 CD-L1, 0.8821 F-score, and 68.86% IoU, which is equal to gains of 1.377, 0.0466, and 3.59% over the previous methods using NASA3D, respectively.

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Section: Reconstruction Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Implementation Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Learning Implicit Neural Representation for Satellite Object Mesh Reconstruction

Yang,

Cao,

et al. 2023

Remote Sensing

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“…Points2Surf [78] learns features from both local patches and the global surface, and reconstructs the surface with an implicit decoder, where the former takes a local encoder to learn the absolute distance of a queried point from the local surfaces, and the latter learns the interior/exterior of the surface with a global encoder. POCO [102] and [103] encodes each input point into a single latent code, and then perform weighted interpolation among a point and its neighboring ones to get the local latent code.…”

Section: Learning Priors As Local Shape Primitivesmentioning

confidence: 99%

Surface Reconstruction from Point Clouds: A Survey and a Benchmark

Huang¹,

Wen²,

Wang³

et al. 2022

Preprint

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Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem in computer vision and graphics research. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object-and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identify the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular in the research community, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research. We make the benchmark publicly accessible at https://Gorilla-Lab-SCUT.github.io/SurfaceReconstructionBenchmark.

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POCO: Point Convolution for Surface Reconstruction

Cited by 2 publications

References 92 publications

Learning Implicit Neural Representation for Satellite Object Mesh Reconstruction

Learning Implicit Neural Representation for Satellite Object Mesh Reconstruction

Surface Reconstruction from Point Clouds: A Survey and a Benchmark

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