AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
- URL: http://arxiv.org/abs/2108.05836v1
- Date: Thu, 12 Aug 2021 16:37:24 GMT
- Title: AdaFit: Rethinking Learning-based Normal Estimation on Point Clouds
- Authors: Runsong Zhu, Yuan Liu, Zhen Dong, Tengping Jiang, Yuan Wang, Wenping
Wang, Bisheng Yang
- Abstract summary: This paper presents a neural network for robust normal estimation on point clouds, named AdaFit, that can deal with point clouds with noise and density variations.
Existing works use a network to learn point-wise weights for weighted least surface fitting to estimate the normals.
We propose a simple yet effective solution that adds an additional offset prediction to improve the quality of normal estimation.
- Score: 31.641383879577894
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: This paper presents a neural network for robust normal estimation on point
clouds, named AdaFit, that can deal with point clouds with noise and density
variations. Existing works use a network to learn point-wise weights for
weighted least squares surface fitting to estimate the normals, which has
difficulty in finding accurate normals in complex regions or containing noisy
points. By analyzing the step of weighted least squares surface fitting, we
find that it is hard to determine the polynomial order of the fitting surface
and the fitting surface is sensitive to outliers. To address these problems, we
propose a simple yet effective solution that adds an additional offset
prediction to improve the quality of normal estimation. Furthermore, in order
to take advantage of points from different neighborhood sizes, a novel Cascaded
Scale Aggregation layer is proposed to help the network predict more accurate
point-wise offsets and weights. Extensive experiments demonstrate that AdaFit
achieves state-of-the-art performance on both the synthetic PCPNet dataset and
the real-word SceneNN dataset.
Related papers
- OCMG-Net: Neural Oriented Normal Refinement for Unstructured Point Clouds [18.234146052486054]
We present a robust refinement method for estimating oriented normals from unstructured point clouds.
Our framework incorporates sign orientation and data augmentation in the feature space to refine the initial oriented normals.
To address the issue of noise-caused direction inconsistency existing in previous approaches, we introduce a new metric called the Chamfer Normal Distance.
arXiv Detail & Related papers (2024-09-02T09:30:02Z) - Arbitrary-Scale Point Cloud Upsampling by Voxel-Based Network with
Latent Geometric-Consistent Learning [52.825441454264585]
We propose an arbitrary-scale Point cloud Upsampling framework using Voxel-based Network (textbfPU-VoxelNet)
Thanks to the completeness and regularity inherited from the voxel representation, voxel-based networks are capable of providing predefined grid space to approximate 3D surface.
A density-guided grid resampling method is developed to generate high-fidelity points while effectively avoiding sampling outliers.
arXiv Detail & Related papers (2024-03-08T07:31:14Z) - CMG-Net: Robust Normal Estimation for Point Clouds via Chamfer Normal
Distance and Multi-scale Geometry [23.86650228464599]
This work presents an accurate and robust method for estimating normals from point clouds.
We first propose a new metric termed Chamfer Normal Distance to address this issue.
We devise an innovative architecture that encompasses Multi-scale Local Feature Aggregation and Hierarchical Geometric Information Fusion.
arXiv Detail & Related papers (2023-12-14T17:23:16Z) - NeuralGF: Unsupervised Point Normal Estimation by Learning Neural
Gradient Function [55.86697795177619]
Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing.
We introduce a new paradigm for learning neural gradient functions, which encourages the neural network to fit the input point clouds.
Our excellent results on widely used benchmarks demonstrate that our method can learn more accurate normals for both unoriented and oriented normal estimation tasks.
arXiv Detail & Related papers (2023-11-01T09:25:29Z) - Rethinking the Approximation Error in 3D Surface Fitting for Point Cloud
Normal Estimation [39.79759035338819]
We present two basic design principles to bridge the gap between estimated and precise surface normals.
We implement these two principles using deep neural networks, and integrate them with the state-of-the-art (SOTA) normal estimation methods in a plug-and-play manner.
arXiv Detail & Related papers (2023-03-30T05:59:43Z) - HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper
Surfaces [54.77683371400133]
We propose a novel normal estimation method called HSurf-Net, which can accurately predict normals from point clouds with noise and density variations.
Experimental results show that our HSurf-Net achieves the state-of-the-art performance on the synthetic shape dataset.
arXiv Detail & Related papers (2022-10-13T16:39:53Z) - Adaptive Self-supervision Algorithms for Physics-informed Neural
Networks [59.822151945132525]
Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function.
We study the impact of the location of the collocation points on the trainability of these models.
We propose a novel adaptive collocation scheme which progressively allocates more collocation points to areas where the model is making higher errors.
arXiv Detail & Related papers (2022-07-08T18:17:06Z) - PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface.
Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context.
We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
arXiv Detail & Related papers (2021-07-13T07:45:48Z) - Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
We propose a novel end-to-end learning-based framework to generate dense point clouds.
We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors.
Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
arXiv Detail & Related papers (2020-11-25T14:00:18Z) - DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares [43.24287146191367]
We propose a surface fitting method for unstructured 3D point clouds.
This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares surface fitting.
arXiv Detail & Related papers (2020-03-23T09:18:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.