PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling

• URL: http://arxiv.org/abs/2305.01148v1
• Date: Tue, 2 May 2023 01:42:47 GMT
• Title: PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling
• Authors: Dohoon Kim, Minwoo Shin, Joonki Paik
• Abstract summary: We present a combined graph convolution and transformer for point cloud upsampling, denoted by PU-EdgeFormer. We employ graph convolution using EdgeConv, which learns the local geometry and global structure of point cloud better than existing point-to-feature method.
• Score: 4.125187280299247
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite the recent development of deep learning-based point cloud upsampling, most MLP-based point cloud upsampling methods have limitations in that it is difficult to train the local and global structure of the point cloud at the same time. To solve this problem, we present a combined graph convolution and transformer for point cloud upsampling, denoted by PU-EdgeFormer. The proposed method constructs EdgeFormer unit that consists of graph convolution and multi-head self-attention modules. We employ graph convolution using EdgeConv, which learns the local geometry and global structure of point cloud better than existing point-to-feature method. Through in-depth experiments, we confirmed that the proposed method has better point cloud upsampling performance than the existing state-of-the-art method in both subjective and objective aspects. The code is available at https://github.com/dohoon2045/PU-EdgeFormer.

Related papers

• P2P-Bridge: Diffusion Bridges for 3D Point Cloud Denoising [81.92854168911704]
  We tackle the task of point cloud denoising through a novel framework that adapts Diffusion Schr"odinger bridges to points clouds. Experiments on object datasets show that P2P-Bridge achieves significant improvements over existing methods.
  arXiv  Detail & Related papers  (2024-08-29T08:00:07Z)
• Patch-Wise Point Cloud Generation: A Divide-and-Conquer Approach [83.05340155068721]
  We devise a new 3d point cloud generation framework using a divide-and-conquer approach. All patch generators are based on learnable priors, which aim to capture the information of geometry primitives. Experimental results on a variety of object categories from the most popular point cloud dataset, ShapeNet, show the effectiveness of the proposed patch-wise point cloud generation.
  arXiv  Detail & Related papers  (2023-07-22T11:10:39Z)
• HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion [15.94976936555104]
  We propose a cross-source point cloud fusion algorithm called HybridFusion. It can register cross-source dense point clouds from different viewing angle in outdoor large scenes. The proposed approach is evaluated comprehensively through qualitative and quantitative experiments.
  arXiv  Detail & Related papers  (2023-04-10T10:54:54Z)
• BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling [60.257912103351394]
  We develop a new point cloud upsampling pipeline called BIMS-PU. We decompose the up/downsampling procedure into several up/downsampling sub-steps by breaking the target sampling factor into smaller factors. We show that our method achieves superior results to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-25T13:13:37Z)
• PU-EVA: An Edge Vector based Approximation Solution for Flexible-scale Point Cloud Upsampling [4.418205951027186]
  Upsampling sparse, noisy and nonuniform point clouds is a challenging task. A novel design of Edge Vector based Approximation for Flexible-scale Point clouds Upsampling (PU-EVA) is proposed. The EVA upsampling decouples the upsampling scales with network architecture, achieving the flexible upsampling rates in one-time training.
  arXiv  Detail & Related papers  (2022-04-22T15:14:05Z)
• Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation [79.60988242843437]
  We propose a novel approach that achieves self-supervised and magnification-flexible point clouds upsampling simultaneously. Experimental results demonstrate that our self-supervised learning based scheme achieves competitive or even better performance than supervised learning based state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-04-18T07:18:25Z)
• PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [81.71904691925428]
  We present a new method that reformulates point cloud completion as a set-to-set translation problem. We also design a new model, called PoinTr, that adopts a transformer encoder-decoder architecture for point cloud completion. Our method outperforms state-of-the-art methods by a large margin on both the new benchmarks and the existing ones.
  arXiv  Detail & Related papers  (2021-08-19T17:58:56Z)
• SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering [21.563862632172363]
  We propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth. To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.
  arXiv  Detail & Related papers  (2021-08-01T13:26:01Z)
• "Zero Shot" Point Cloud Upsampling [4.737519767218666]
  We present an unsupervised approach to upsample point clouds internally referred as "Zero Shot" Point Cloud Upsampling (ZSPU) at holistic level. Our approach is solely based on the internal information provided by a particular point cloud without patching in both self-training and testing phases. ZSPU achieves superior qualitative results on shapes with complex local details or high curvatures.
  arXiv  Detail & Related papers  (2021-06-25T17:06:18Z)
• FKAConv: Feature-Kernel Alignment for Point Cloud Convolution [75.85619090748939]
  We provide a formulation to relate and analyze a number of point convolution methods. We also propose our own convolution variant, that separates the estimation of geometry-less kernel weights. We show competitive results on classification and semantic segmentation benchmarks.
  arXiv  Detail & Related papers  (2020-04-09T10:12:45Z)

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.