Efficient Point Clouds Upsampling via Flow Matching

• URL: http://arxiv.org/abs/2501.15286v1
• Date: Sat, 25 Jan 2025 17:50:53 GMT
• Title: Efficient Point Clouds Upsampling via Flow Matching
• Authors: Zhi-Song Liu, Chenhang He, Lei Li,
• Abstract summary: Existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds. We propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method delivers superior upsampling quality but with fewer sampling steps.
• Score: 16.948354780275388
• License:
• Abstract: Diffusion models are a powerful framework for tackling ill-posed problems, with recent advancements extending their use to point cloud upsampling. Despite their potential, existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds, overlooking the geometric information inherent in sparse point clouds. To address these inefficiencies, we propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method first employs midpoint interpolation to sparse point clouds, resolving the density mismatch between sparse and dense point clouds. Since point clouds are unordered representations, we introduce a pre-alignment method based on Earth Mover's Distance (EMD) optimization to ensure coherent interpolation between sparse and dense point clouds, which enables a more stable learning path in flow matching. Experiments on synthetic datasets demonstrate that our method delivers superior upsampling quality but with fewer sampling steps. Further experiments on ScanNet and KITTI also show that our approach generalizes well on RGB-D point clouds and LiDAR point clouds, making it more practical for real-world applications.

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)
• Learning Continuous Implicit Field with Local Distance Indicator for Arbitrary-Scale Point Cloud Upsampling [55.05706827963042]
  Point cloud upsampling aims to generate dense and uniformly distributed point sets from a sparse point cloud. Previous methods typically split a sparse point cloud into several local patches, upsample patch points, and merge all upsampled patches. We propose a novel approach that learns an unsigned distance field guided by local priors for point cloud upsampling.
  arXiv  Detail & Related papers  (2023-12-23T01:52:14Z)
• A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling [10.390581335119098]
  We propose a conditional denoising diffusion probability model (DDPM) for point cloud upsampling, called PUDM. PUDM treats the sparse point cloud as a condition, and iteratively learns the transformation relationship between the dense point cloud and the noise. PUDM exhibits strong noise robustness in experimental results.
  arXiv  Detail & Related papers  (2023-12-03T12:41:41Z)
• 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)
• 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)
• Controllable Mesh Generation Through Sparse Latent Point Diffusion Models [105.83595545314334]
  We design a novel sparse latent point diffusion model for mesh generation. Our key insight is to regard point clouds as an intermediate representation of meshes, and model the distribution of point clouds instead. Our proposed sparse latent point diffusion model achieves superior performance in terms of generation quality and controllability.
  arXiv  Detail & Related papers  (2023-03-14T14:25:29Z)
• 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)
• 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)
• DeepCLR: Correspondence-Less Architecture for Deep End-to-End Point Cloud Registration [12.471564670462344]
  This work addresses the problem of point cloud registration using deep neural networks. We propose an approach to predict the alignment between two point clouds with overlapping data content, but displaced origins. Our approach achieves state-of-the-art accuracy and the lowest run-time of the compared methods.
  arXiv  Detail & Related papers  (2020-07-22T08:20:57Z)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)

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.