Refining 3D Point Cloud Normal Estimation via Sample Selection

• URL: http://arxiv.org/abs/2406.18541v1
• Date: Mon, 20 May 2024 02:06:10 GMT
• Title: Refining 3D Point Cloud Normal Estimation via Sample Selection
• Authors: Jun Zhou, Yaoshun Li, Hongchen Tan, Mingjie Wang, Nannan Li, Xiuping Liu,
• Abstract summary: We introduce a fundamental framework for normal estimation, enhancing existing model through the incorporation of global information and various constraint mechanisms. We also utilize existing orientation methods to correct estimated non-oriented normals, achieving state-of-the-art performance in both oriented and non-oriented tasks.
• Score: 13.207964615561261
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, point cloud normal estimation, as a classical and foundational algorithm, has garnered extensive attention in the field of 3D geometric processing. Despite the remarkable performance achieved by current Neural Network-based methods, their robustness is still influenced by the quality of training data and the models' performance. In this study, we designed a fundamental framework for normal estimation, enhancing existing model through the incorporation of global information and various constraint mechanisms. Additionally, we employed a confidence-based strategy to select the reasonable samples for fair and robust network training. The introduced sample confidence can be integrated into the loss function to balance the influence of different samples on model training. Finally, we utilized existing orientation methods to correct estimated non-oriented normals, achieving state-of-the-art performance in both oriented and non-oriented tasks. Extensive experimental results demonstrate that our method works well on the widely used benchmarks.

Related papers

• iNeMo: Incremental Neural Mesh Models for Robust Class-Incremental Learning [22.14627083675405]
  We propose incremental neural mesh models that can be extended with new meshes over time. We demonstrate the effectiveness of our method through extensive experiments on the Pascal3D and ObjectNet3D datasets. Our work also presents the first incremental learning approach for pose estimation.
  arXiv  Detail & Related papers  (2024-07-12T13:57:49Z)
• Trustworthy Personalized Bayesian Federated Learning via Posterior Fine-Tune [3.1001287855313966]
  We introduce a novel framework for personalized federated learning, incorporating Bayesian methodology. We show that the new algorithm not only improves accuracy but also outperforms the baseline significantly in OOD detection.
  arXiv  Detail & Related papers  (2024-02-25T13:28:08Z)
• Consistency Regularization for Generalizable Source-free Domain Adaptation [62.654883736925456]
  Source-free domain adaptation (SFDA) aims to adapt a well-trained source model to an unlabelled target domain without accessing the source dataset. Existing SFDA methods ONLY assess their adapted models on the target training set, neglecting the data from unseen but identically distributed testing sets. We propose a consistency regularization framework to develop a more generalizable SFDA method.
  arXiv  Detail & Related papers  (2023-08-03T07:45:53Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [89.54947228958494]
  This paper focuses on the fine-tuning of an adversarially pre-trained model in various classification tasks. We propose a novel statistics-based approach, Two-WIng NormliSation (TWINS) fine-tuning framework. TWINS is shown to be effective on a wide range of image classification datasets in terms of both generalization and robustness.
  arXiv  Detail & Related papers  (2023-03-20T14:12:55Z)
• Cross-Modal Fine-Tuning: Align then Refine [83.37294254884446]
  ORCA is a cross-modal fine-tuning framework that extends the applicability of a single large-scale pretrained model to diverse modalities. We show that ORCA obtains state-of-the-art results on 3 benchmarks containing over 60 datasets from 12 modalities.
  arXiv  Detail & Related papers  (2023-02-11T16:32:28Z)
• Predicting Out-of-Distribution Error with Confidence Optimal Transport [17.564313038169434]
  We present a simple yet effective method to predict a model's performance on an unknown distribution without any addition annotation. We show that our method, Confidence Optimal Transport (COT), provides robust estimates of a model's performance on a target domain. Despite its simplicity, our method achieves state-of-the-art results on three benchmark datasets and outperforms existing methods by a large margin.
  arXiv  Detail & Related papers  (2023-02-10T02:27:13Z)
• On Modality Bias Recognition and Reduction [70.69194431713825]
  We study the modality bias problem in the context of multi-modal classification. We propose a plug-and-play loss function method, whereby the feature space for each label is adaptively learned. Our method yields remarkable performance improvements compared with the baselines.
  arXiv  Detail & Related papers  (2022-02-25T13:47:09Z)
• Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation [87.54604263202941]
  We propose a tiny deep neural network of which partial layers are iteratively exploited for refining its previous estimations. We employ learned gating criteria to decide whether to exit from the weight-sharing loop, allowing per-sample adaptation in our model. Our method consistently outperforms state-of-the-art 2D/3D hand pose estimation approaches in terms of both accuracy and efficiency for widely used benchmarks.
  arXiv  Detail & Related papers  (2021-11-11T23:31:34Z)
• Learning Neural Models for Natural Language Processing in the Face of Distributional Shift [10.990447273771592]
  The dominating NLP paradigm of training a strong neural predictor to perform one task on a specific dataset has led to state-of-the-art performance in a variety of applications. It builds upon the assumption that the data distribution is stationary, ie. that the data is sampled from a fixed distribution both at training and test time. This way of training is inconsistent with how we as humans are able to learn from and operate within a constantly changing stream of information. It is ill-adapted to real-world use cases where the data distribution is expected to shift over the course of a model's lifetime
  arXiv  Detail & Related papers  (2021-09-03T14:29:20Z)
• Deep Optimized Priors for 3D Shape Modeling and Reconstruction [38.79018852887249]
  We introduce a new learning framework for 3D modeling and reconstruction. We show that the proposed strategy effectively breaks the barriers constrained by the pre-trained priors.
  arXiv  Detail & Related papers  (2020-12-14T03:56:31Z)

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.