PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds

• URL: http://arxiv.org/abs/2012.00987v2
• Date: Wed, 12 May 2021 08:44:23 GMT
• Title: PV-RAFT: Point-Voxel Correlation Fields for Scene Flow Estimation of Point Clouds
• Authors: Yi Wei, Ziyi Wang, Yongming Rao, Jiwen Lu, Jie Zhou
• Abstract summary: We propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets.
• Score: 89.76093400242088
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a Point-Voxel Recurrent All-Pairs Field Transforms (PV-RAFT) method to estimate scene flow from point clouds. Since point clouds are irregular and unordered, it is challenging to efficiently extract features from all-pairs fields in the 3D space, where all-pairs correlations play important roles in scene flow estimation. To tackle this problem, we present point-voxel correlation fields, which capture both local and long-range dependencies of point pairs. To capture point-based correlations, we adopt the K-Nearest Neighbors search that preserves fine-grained information in the local region. By voxelizing point clouds in a multi-scale manner, we construct pyramid correlation voxels to model long-range correspondences. Integrating these two types of correlations, our PV-RAFT makes use of all-pairs relations to handle both small and large displacements. We evaluate the proposed method on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Experimental results show that PV-RAFT outperforms state-of-the-art methods by remarkable margins.

Related papers

• Self-Supervised Scene Flow Estimation with Point-Voxel Fusion and Surface Representation [30.355128117680444]
  Scene flow estimation aims to generate the 3D motion field of points between two consecutive frames of point clouds. Existing point-based methods ignore the irregularity of point clouds and have difficulty capturing long-range dependencies. We propose a point-voxel fusion method, where we utilize a voxel branch based on sparse grid attention and the shifted window strategy to capture long-range dependencies.
  arXiv  Detail & Related papers  (2024-10-17T09:05:15Z)
• PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction [72.75478398447396]
  We propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively. Considering the distance distribution of LiDAR point clouds, we construct the tri-perspective view in the cylindrical coordinate system. We employ spatial group pooling to maintain structural details during projection and adopt 2D backbones to efficiently process each TPV plane.
  arXiv  Detail & Related papers  (2023-08-31T17:57:17Z)
• A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation [12.499361832561634]
  We present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation. Our method significantly outperforms existing methods on overlap prediction, especially in scenes with small overlaps.
  arXiv  Detail & Related papers  (2023-02-28T12:01:16Z)
• PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds [49.7285297470392]
  An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. It decomposes the scene flow estimation task into a set of subtasks, including ego-motion compensation, object association and object-wise motion estimation.
  arXiv  Detail & Related papers  (2023-02-27T23:06:01Z)
• SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow [25.577386156273256]
  Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision.
  arXiv  Detail & Related papers  (2022-11-25T10:52:02Z)
• IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment [58.8330387551499]
  We formulate the problem as estimation of point-wise trajectories (i.e., smooth curves) We propose IDEA-Net, an end-to-end deep learning framework, which disentangles the problem under the assistance of the explicitly learned temporal consistency. We demonstrate the effectiveness of our method on various point cloud sequences and observe large improvement over state-of-the-art methods both quantitatively and visually.
  arXiv  Detail & Related papers  (2022-03-22T10:14:08Z)
• Residual 3D Scene Flow Learning with Context-Aware Feature Extraction [11.394559627312743]
  We propose a novel context-aware set conv layer to exploit contextual structure information of Euclidean space. We also propose an explicit residual flow learning structure in the residual flow refinement layer to cope with long-distance movement. Our method achieves state-of-the-art performance, surpassing all other previous works to the best of our knowledge by at least 25%.
  arXiv  Detail & Related papers  (2021-09-10T06:15:18Z)
• PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
  We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context. We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
  arXiv  Detail & Related papers  (2021-07-13T07:45:48Z)
• SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation [71.2856098776959]
  Estimating 3D motions for point clouds is challenging, since a point cloud is unordered and its density is significantly non-uniform. We propose a novel architecture named Sparse Convolution-Transformer Network (SCTN) that equips the sparse convolution with the transformer. We show that the learned relation-based contextual information is rich and helpful for matching corresponding points, benefiting scene flow estimation.
  arXiv  Detail & Related papers  (2021-05-10T15:16:14Z)

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.