A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation

• URL: http://arxiv.org/abs/2302.14511v1
• Date: Tue, 28 Feb 2023 12:01:16 GMT
• Title: A Unified BEV Model for Joint Learning of 3D Local Features and Overlap Estimation
• Authors: Lin Li, Wendong Ding, Yongkun Wen, Yufei Liang, Yong Liu, Guowei Wan
• Abstract summary: 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.
• Score: 12.499361832561634
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pairwise point cloud registration is a critical task for many applications, which heavily depends on finding the right correspondences from the two point clouds. However, the low overlap between the input point clouds makes the registration prone to fail, leading to mistaken overlapping and mismatched correspondences, especially in scenes where non-overlapping regions contain similar structures. In this paper, we present a unified bird's-eye view (BEV) model for jointly learning of 3D local features and overlap estimation to fulfill the pairwise registration and loop closure. Feature description based on BEV representation is performed by a sparse UNet-like network, and the 3D keypoints are extracted by a detection head for 2D locations and a regression head for heights, respectively. For overlap detection, a cross-attention module is applied for interacting contextual information of the input point clouds, followed by a classification head to estimate the overlapping region. We evaluate our unified model extensively on the KITTI dataset and Apollo-SouthBay dataset. The experiments demonstrate that our method significantly outperforms existing methods on overlap prediction, especially in scenes with small overlaps. The registration precision also achieves top performance on both datasets in terms of translation and rotation errors. Source codes will be available soon.

Related papers

• Equi-GSPR: Equivariant SE(3) Graph Network Model for Sparse Point Cloud Registration [2.814748676983944]
  We propose a graph neural network model embedded with a local Spherical Euclidean 3D equivariance property through SE(3) message passing based propagation. Our model is composed mainly of a descriptor module, equivariant graph layers, match similarity, and the final regression layers. Experiments conducted on the 3DMatch and KITTI datasets exhibit the compelling and robust performance of our model compared to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-10-08T06:48:01Z)
• Instance-free Text to Point Cloud Localization with Relative Position Awareness [37.22900045434484]
  Text-to-point-cloud cross-modal localization is an emerging vision-language task critical for future robot-human collaboration. We address two key limitations of existing approaches: 1) their reliance on ground-truth instances as input; and 2) their neglect of the relative positions among potential instances. Our proposed model follows a two-stage pipeline, including a coarse stage for text-cell retrieval and a fine stage for position estimation.
  arXiv  Detail & Related papers  (2024-04-27T09:46:49Z)
• Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation [78.6612285236938]
  We propose a novel DAT (textbfDual textbfAdaptive textbfTransformations) model for weakly supervised point cloud segmentation. We evaluate our proposed DAT model with two popular backbones on the large-scale S3DIS and ScanNet-V2 datasets.
  arXiv  Detail & Related papers  (2022-07-19T05:43:14Z)
• CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence [51.91791056908387]
  We propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P. Specifically, given a pair of a 2D image before a 3D point cloud, we first transform them into high-dimensional feature space feed the features into a symmetric overlapping region to determine the region where the image point cloud overlap.
  arXiv  Detail & Related papers  (2022-07-12T11:49:31Z)
• Learning to Register Unbalanced Point Pairs [10.369750912567714]
  Recent 3D registration methods can effectively handle large-scale or partially overlapping point pairs. We present a novel 3D registration method, called UPPNet, for the unbalanced point pairs.
  arXiv  Detail & Related papers  (2022-07-09T08:03:59Z)
• Focal Sparse Convolutional Networks for 3D Object Detection [121.45950754511021]
  We introduce two new modules to enhance the capability of Sparse CNNs. They are focal sparse convolution (Focals Conv) and its multi-modal variant of focal sparse convolution with fusion. For the first time, we show that spatially learnable sparsity in sparse convolution is essential for sophisticated 3D object detection.
  arXiv  Detail & Related papers  (2022-04-26T17:34:10Z)
• iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
  iSDF is a continuous learning system for real-time signed distance field reconstruction. It produces more accurate reconstructions and better approximations of collision costs and gradients.
  arXiv  Detail & Related papers  (2022-04-05T15:48:39Z)
• DFC: Deep Feature Consistency for Robust Point Cloud Registration [0.4724825031148411]
  We present a novel learning-based alignment network for complex alignment scenes. We validate our approach on the 3DMatch dataset and the KITTI odometry dataset.
  arXiv  Detail & Related papers  (2021-11-15T08:27:21Z)
• Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based Perception [122.53774221136193]
  State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution. A natural remedy is to utilize the 3D voxelization and 3D convolution network. We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
  arXiv  Detail & Related papers  (2021-09-12T06:25:11Z)
• D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features [51.04841465193678]
  We leverage a 3D fully convolutional network for 3D point clouds. We propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. Our method achieves state-of-the-art results in both indoor and outdoor scenarios.
  arXiv  Detail & Related papers  (2020-03-06T12:51:09Z)

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.