HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion
- URL: http://arxiv.org/abs/2304.04508v1
- Date: Mon, 10 Apr 2023 10:54:54 GMT
- Title: HybridFusion: LiDAR and Vision Cross-Source Point Cloud Fusion
- Authors: Yu Wang, Shuhui Bu, Lin Chen, Yifei Dong, Kun Li, Xuefeng Cao, Ke Li
- Abstract summary: 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.
- Score: 15.94976936555104
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, cross-source point cloud registration from different sensors has
become a significant research focus. However, traditional methods confront
challenges due to the varying density and structure of cross-source point
clouds. In order to solve these problems, 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 entire
registration process is a coarse-to-fine procedure. First, the point cloud is
divided into small patches, and a matching patch set is selected based on
global descriptors and spatial distribution, which constitutes the coarse
matching process. To achieve fine matching, 2D registration is performed by
extracting 2D boundary points from patches, followed by 3D adjustment. Finally,
the results of multiple patch pose estimates are clustered and fused to
determine the final pose. The proposed approach is evaluated comprehensively
through qualitative and quantitative experiments. In order to compare the
robustness of cross-source point cloud registration, the proposed method and
generalized iterative closest point method are compared. Furthermore, a metric
for describing the degree of point cloud filling is proposed. The experimental
results demonstrate that our approach achieves state-of-the-art performance in
cross-source point cloud registration.
Related papers
- Fully-Geometric Cross-Attention for Point Cloud Registration [51.865371511201765]
Point cloud registration approaches often fail when the overlap between point clouds is low due to noisy point correspondences.
This work introduces a novel cross-attention mechanism tailored for Transformer-based architectures that tackles this problem.
We integrate the Gromov-Wasserstein distance into the cross-attention formulation to jointly compute distances between points across different point clouds.
At the point level, we also devise a self-attention mechanism that aggregates the local geometric structure information into point features for fine matching.
arXiv Detail & Related papers (2025-02-12T10:44:36Z) - Efficient Point Clouds Upsampling via Flow Matching [16.948354780275388]
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.
arXiv Detail & Related papers (2025-01-25T17:50:53Z) - Multiway Point Cloud Mosaicking with Diffusion and Global Optimization [74.3802812773891]
We introduce a novel framework for multiway point cloud mosaicking (named Wednesday)
At the core of our approach is ODIN, a learned pairwise registration algorithm that identifies overlaps and refines attention scores.
Tested on four diverse, large-scale datasets, our method state-of-the-art pairwise and rotation registration results by a large margin on all benchmarks.
arXiv Detail & Related papers (2024-03-30T17:29:13Z) - 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) - 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) - 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) - PCAM: Product of Cross-Attention Matrices for Rigid Registration of
Point Clouds [79.99653758293277]
PCAM is a neural network whose key element is a pointwise product of cross-attention matrices.
We show that PCAM achieves state-of-the-art results among methods which, like us, solve steps (a) and (b) jointly via deepnets.
arXiv Detail & Related papers (2021-10-04T09:23:27Z) - 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) - Point Set Voting for Partial Point Cloud Analysis [26.31029112502835]
techniques for point cloud classification and segmentation have in recent years achieved incredible performance driven in part by leveraging large synthetic datasets.
This paper proposes a general model for partial point clouds analysis wherein the latent feature encoding a complete point clouds is inferred by applying a local point set voting strategy.
arXiv Detail & Related papers (2020-07-09T03:37:31Z) - Learning multiview 3D point cloud registration [74.39499501822682]
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Our approach outperforms the state-of-the-art by a significant margin, while being end-to-end trainable and computationally less costly.
arXiv Detail & Related papers (2020-01-15T03:42:14Z)
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.