DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification

• URL: http://arxiv.org/abs/2504.12456v1
• Date: Wed, 16 Apr 2025 19:43:32 GMT
• Title: DG-MVP: 3D Domain Generalization via Multiple Views of Point Clouds for Classification
• Authors: Huantao Ren, Minmin Yang, Senem Velipasalar,
• Abstract summary: Deep neural networks have achieved significant success in 3D point cloud classification.<n>In this paper, we focus on the 3D point cloud domain generalization problem.<n>We propose a novel method for 3D point cloud domain generalization, which can generalize to unseen domains of point clouds.
• Score: 10.744510913722817
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep neural networks have achieved significant success in 3D point cloud classification while relying on large-scale, annotated point cloud datasets, which are labor-intensive to build. Compared to capturing data with LiDAR sensors and then performing annotation, it is relatively easier to sample point clouds from CAD models. Yet, data sampled from CAD models is regular, and does not suffer from occlusion and missing points, which are very common for LiDAR data, creating a large domain shift. Therefore, it is critical to develop methods that can generalize well across different point cloud domains. %In this paper, we focus on the 3D point cloud domain generalization problem. Existing 3D domain generalization methods employ point-based backbones to extract point cloud features. Yet, by analyzing point utilization of point-based methods and observing the geometry of point clouds from different domains, we have found that a large number of point features are discarded by point-based methods through the max-pooling operation. This is a significant waste especially considering the fact that domain generalization is more challenging than supervised learning, and point clouds are already affected by missing points and occlusion to begin with. To address these issues, we propose a novel method for 3D point cloud domain generalization, which can generalize to unseen domains of point clouds. Our proposed method employs multiple 2D projections of a 3D point cloud to alleviate the issue of missing points and involves a simple yet effective convolution-based model to extract features. The experiments, performed on the PointDA-10 and Sim-to-Real benchmarks, demonstrate the effectiveness of our proposed method, which outperforms different baselines, and can transfer well from synthetic domain to real-world domain.

Related papers

• PIVOT-Net: Heterogeneous Point-Voxel-Tree-based Framework for Point Cloud Compression [8.778300313732027]
  We propose a heterogeneous point cloud compression (PCC) framework. We unify typical point cloud representations -- point-based, voxel-based, and tree-based representations -- and their associated backbones. We augment the framework with a proposed context-aware upsampling for decoding and an enhanced voxel transformer for feature aggregation.
  arXiv  Detail & Related papers  (2024-02-11T16:57:08Z)
• InvariantOODG: Learning Invariant Features of Point Clouds for Out-of-Distribution Generalization [17.96808017359983]
  We propose InvariantOODG, which learns invariability between point clouds with different distributions. We define a set of learnable anchor points that locate the most useful local regions and two types of transformations to augment the input point clouds. The experimental results demonstrate the effectiveness of the proposed model on 3D domain generalization benchmarks.
  arXiv  Detail & Related papers  (2024-01-08T09:41:22Z)
• Point Cloud Pre-training with Diffusion Models [62.12279263217138]
  We propose a novel pre-training method called Point cloud Diffusion pre-training (PointDif) PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification, segmentation and detection.
  arXiv  Detail & Related papers  (2023-11-25T08:10:05Z)
• StarNet: Style-Aware 3D Point Cloud Generation [82.30389817015877]
  StarNet is able to reconstruct and generate high-fidelity and even 3D point clouds using a mapping network. Our framework achieves comparable state-of-the-art performance on various metrics in the point cloud reconstruction and generation tasks.
  arXiv  Detail & Related papers  (2023-03-28T08:21:44Z)
• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling [52.464516118826765]
  We introduce RandLA-Net, an efficient and lightweight neural architecture to infer per-point semantics for large-scale point clouds. The key to our approach is to use random point sampling instead of more complex point selection approaches. Our RandLA-Net can process 1 million points in a single pass up to 200x faster than existing approaches.
  arXiv  Detail & Related papers  (2021-07-06T05:08:34Z)
• OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration [31.108056345511976]
  OMNet is a global feature based iterative network for partial-to-partial point cloud registration. We learn masks in a coarse-to-fine manner to reject non-overlapping regions, which converting the partial-to-partial registration to the registration of the same shapes.
  arXiv  Detail & Related papers  (2021-03-01T11:59:59Z)
• GRNet: Gridding Residual Network for Dense Point Cloud Completion [54.43648460932248]
  Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. We propose a novel Gridding Residual Network (GRNet) for point cloud completion. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.
  arXiv  Detail & Related papers  (2020-06-06T02:46:39Z)

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.