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Noise is an inevitable aspect of point cloud acquisition, necessitating filtering as a fundamental task within the realm of 3D vision.
A volumetric method for building complex models from range images
Curless, B.; and Levoy, M. 1996 · 1996
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Fitting smooth surfaces to dense polygon meshes
Krishnamurthy, V.; and Levoy, M. 1996 · 1996
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K-nearest neighbor
Peterson, L. E. 2009 · 2009
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Paris-rue-Madame database: A 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods
Serna, A.; Marcotegui, B.; Goulette, F.; and Deschaud, J.-E. 2014 · 2014
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Pcpnet learning local shape properties from raw point clouds
Guerrero, P.; Kleiman, Y.; Ovsjanikov, M.; and Mitra, N. J. 2018 · 2018
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Unsupervised learning of shape and pose with differentiable point clouds
Insafutdinov, E.; and Dosovitskiy, A. 2018 · 2018
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Pu-net: Point cloud upsampling network
Yu, L.; Li, X.; Fu, C.-W.; Cohen-Or, D.; and Heng, P.-A. 2018 · 2018
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Soft rasterizer: A differentiable renderer for image-based 3d reasoning
Liu, S.; Li, T.; Chen, W.; and Li, H. 2019 · 2019
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Differentiable surface splatting for point-based geometry processing
Yifan, W.; Serena, F.; Wu, S.; Öztireli, C.; and Sorkine-Hornung, O. 2019 · 2019
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Modular primitives for high-performance differentiable rendering
Laine, S.; Hellsten, J.; Karras, T.; Seol, Y.; Lehtinen, J.; and Aila, T. 2020 · 2020
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Differentiable manifold reconstruction for point cloud denoising
Luo, S.; and Hu, W. 2020 · 2020
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Learning graph-convolutional representations for point cloud denoising
Pistilli, F.; Fracastoro, G.; Valsesia, D.; and Magli, E. 2020 · 2020
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Pointcleannet: Learning to denoise and remove outliers from dense point clouds
Rakotosaona, M.-J.; La Barbera, V.; Guerrero, P.; Mitra, N. J.; and Ovsjanikov, M. 2020 · 2020
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Efficiently modeling long sequences with structured state spaces
Gu, A.; Goel, K.; and Ré, C. 2021 · 2021
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Score-based point cloud denoising
Luo, S.; and Hu, W. 2021 · 2021
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; and Ng, R. 2021 · 2021
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Differentiable rendering of neural sdfs through reparameterization
Bangaru, S. P.; Gharbi, M.; Luan, F.; Li, T.-M.; Sunkavalli, K.; Hasan, M.; Bi, S.; Xu, Z.; Bernstein, G.; and Durand, F. 2022 · 2022
Cited alongside, same era.
Mamba: Linear-time sequence modeling with selective state spaces
Gu, A.; and Dao, T. 2023 · 2023
Later among the works it cites.
3d gaussian splatting for real-time radiance field rendering
Kerbl, B.; Kopanas, G.; Leimkühler, T.; and Drettakis, G. 2023 · 2023
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Volrecon: Volume rendering of signed ray distance functions for generalizable multi-view reconstruction
Ren, Y.; Zhang, T.; Pollefeys, M.; Süsstrunk, S.; and Wang, F. 2023 · 2023
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DenseMamba: State Space Models with Dense Hidden Connection for Efficient Large Language Models
He, W.; Han, K.; Tang, Y.; Wang, C.; Yang, Y.; Guo, T.; and Wang, Y. 2024 · 2024
Closest in time.
PointMamba: A Simple State Space Model for Point Cloud Analysis
Liang, D.; Zhou, X.; Wang, X.; Zhu, X.; Xu, W.; Zou, Z.; Ye, X.; and Bai, X. 2024 · 2024
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Comprehensive review of deep learning-based 3d point cloud completion processing and analysis
Fei, B.; Yang, W.; Chen, W.-M.; Li, Z.; Li, Y.; Ma, T.; Hu, X.; and Ma, L. 2022 · 2022
Cited alongside, same era.
Pd-flow: A point cloud denoising framework with normalizing flows
Mao, A.; Du, Z.; Wen, Y.-H.; Xuan, J.; and Liu, Y.-J. 2022 · 2022
Cited alongside, same era.
Unbiased Gradient Estimation for Differentiable Surface Splatting via Poisson Sampling
Müller, J. U.; Weinmann, M.; and Klein, R. 2022 · 2022
Cited alongside, same era.
Differentiable signed distance function rendering
Vicini, D.; Speierer, S.; and Jakob, W. 2022 · 2022
Cited alongside, same era.
Fast and accurate normal estimation for point clouds via patch stitching
Zhou, J.; Jin, W.; Wang, M.; Liu, X.; Li, Z.; and Liu, Z. 2022 · 2022
Cited alongside, same era.
IterativePFN: True iterative point cloud filtering
de Silva Edirimuni, D.; Lu, X.; Shao, Z.; Li, G.; Robles-Kelly, A.; and He, Y. 2023 · 2023
Cited alongside, same era.
Self-supervised learning for pre-training 3d point clouds: A survey
Fei, B.; Yang, W.; Liu, L.; Luo, T.; Zhang, R.; Li, Y.; and He, Y. 2023 · 2023
Cited alongside, same era.
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LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image Segmentation
Liao, W.; Zhu, Y.; Wang, X.; Pan, C.; Wang, Y.; and Ma, L. 2024 · 2024
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Structured state space models for in-context reinforcement learning
Lu, C.; Schroecker, Y.; Gu, A.; Parisotto, E.; Foerster, J.; Singh, S.; and Behbahani, F. 2024 · 2024
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On the low-shot transferability of [V]-Mamba
Misra, D.; Gala, J.; and Orvieto, A. 2024 · 2024
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ClinicalMamba: A Generative Clinical Language Model on Longitudinal Clinical Notes
Yang, Z.; Mitra, A.; Kwon, S.; and Yu, H. 2024 · 2024
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Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference
Zhao, H.; Zhang, M.; Zhao, W.; Ding, P.; Huang, S.; and Wang, D. 2024 · 2024
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Pointfilter: Point cloud filtering via encoder-decoder modeling
Zhang, D.; Lu, X.; Qin, H.; and He, Y. 2020 · 2027
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