MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

Sarode, Vinit; Dhagat, Animesh; Srivatsan, Rangaprasad Arun; Zevallos, Nicolas; Lucey, Simon; Choset, Howie

doi:10.48550/arxiv.2010.09185

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…MaskNet [ 165 ]: The authors of this model presented MaskNet for determining outlier points in point clouds by computing a mask. The method can be used to reject noise in even partial clouds in a rather computationally inexpensive manner.…”

Section: Augmentationmentioning

confidence: 99%

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

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The research groups in computer vision, graphics, and machine learning have dedicated a substantial amount of attention to the areas of 3D object reconstruction, augmentation, and registration. Deep learning is the predominant method used in artificial intelligence for addressing computer vision challenges. However, deep learning on three-dimensional data presents distinct obstacles and is now in its nascent phase. There have been significant advancements in deep learning specifically for three-dimensional data, offering a range of ways to address these issues. This study offers a comprehensive examination of the latest advancements in deep learning methodologies. We examine many benchmark models for the tasks of 3D object registration, augmentation, and reconstruction. We thoroughly analyse their architectures, advantages, and constraints. In summary, this report provides a comprehensive overview of recent advancements in three-dimensional deep learning and highlights unresolved research areas that will need to be addressed in the future.

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Section: Augmentationmentioning

confidence: 99%

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Vinodkumar,

Karabulut,

Avots

et al. 2024

Entropy

View full text Add to dashboard Cite

show abstract

“…Recently, deep learning has been successfully applied to the point cloud registration. There are some supervised methods (Lu et al 2019;Yuan et al 2020;Sarode et al 2020;Huang et al 2021;Li, Pontes, and Lucey 2021). PointNetLK (Aoki et al 2019) modifies the Lucas & Kanada (LK) algorithm and integrates it into the PointNet (Qi et al 2017) for point cloud registration.…”

Section: Related Workmentioning

confidence: 99%

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

Shen

Hui

Jiang

et al. 2022

AAAI

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Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation.Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance.

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“…Recently, deep learning has been successfully applied to the point cloud registration which can get the rigid transformation in an end-toend manner. There are some supervised methods (Lu et al 2019;Yuan et al 2020;Sarode et al 2020;Huang et al 2021). PointNetLK (Aoki et al 2019) Additionally, there are some end-to-end unsupervised point cloud registration methods (Kadam et al 2020;Feng et al 2021;Li, Wang, and Fang 2019;El Banani, Gao, and Johnson 2021).…”

Section: Related Workmentioning

confidence: 99%

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

Shen¹,

Liu²,

Jiang³

et al. 2022

Preprint

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Unsupervised point cloud registration algorithm usually suffers from the unsatisfied registration precision in the partially overlapping problem due to the lack of effective inlier evaluation. In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration. It is expected to capture the discriminative geometric difference between the source neighborhood and the corresponding pseudo target neighborhood for effective inlier distinction. Specifically, our model consists of a matching map refinement module and an inlier evaluation module. In our matching map refinement module, we improve the point-wise matching map estimation by integrating the matching scores of neighbors into it. The aggregated neighborhood information potentially facilitates the discriminative map construction so that high-quality correspondences can be provided for generating the pseudo target point cloud. Based on the observation that the outlier has the significant structure-wise difference between its source neighborhood and corresponding pseudo target neighborhood while this difference for inlier is small, the inlier evaluation module exploits this difference to score the inlier confidence for each estimated correspondence. In particular, we construct an effective graph representation for capturing this geometric difference between the neighborhoods. Finally, with the learned correspondences and the corresponding inlier confidence, we use the weighted SVD algorithm for transformation estimation. Under the unsupervised setting, we exploit the Huber function based global alignment loss, the local neighborhood consensus loss, and spatial consistency loss for model optimization. The experimental results on extensive datasets demonstrate that our unsupervised point cloud registration method can yield comparable performance. Our code is available at https://github.com/supersyq/RIENet.

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MaskNet: A Fully-Convolutional Network to Estimate Inlier Points

Cited by 3 publications

References 40 publications

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

Reliable Inlier Evaluation for Unsupervised Point Cloud Registration

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