TransLoc3D : Point Cloud based Large-scale Place Recognition using
Adaptive Receptive Fields
- URL: http://arxiv.org/abs/2105.11605v1
- Date: Tue, 25 May 2021 01:54:31 GMT
- Title: TransLoc3D : Point Cloud based Large-scale Place Recognition using
Adaptive Receptive Fields
- Authors: Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, Song-Hai Zhang
- Abstract summary: We argue that fixed receptive fields are not well suited for place recognition.
We propose a novel Adaptive Receptive Field Module (ARFM), which can adaptively adjust the size of the receptive field based on the input point cloud.
We also present a novel network architecture, named TransLoc3D, to obtain discriminative global descriptors of point clouds.
- Score: 40.55971834919629
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Place recognition plays an essential role in the field of autonomous driving
and robot navigation. Although a number of point cloud based methods have been
proposed and achieved promising results, few of them take the size difference
of objects into consideration. For small objects like pedestrians and vehicles,
large receptive fields will capture unrelated information, while small
receptive fields would fail to encode complete geometric information for large
objects such as buildings. We argue that fixed receptive fields are not well
suited for place recognition, and propose a novel Adaptive Receptive Field
Module (ARFM), which can adaptively adjust the size of the receptive field
based on the input point cloud. We also present a novel network architecture,
named TransLoc3D, to obtain discriminative global descriptors of point clouds
for the place recognition task. TransLoc3D consists of a 3D sparse
convolutional module, an ARFM module, an external transformer network which
aims to capture long range dependency and a NetVLAD layer. Experiments show
that our method outperforms prior state-of-the-art results, with an improvement
of 1.1\% on average recall@1 on the Oxford RobotCar dataset, and 0.8\% on the
B.D. dataset.
Related papers
- Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries.
We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images.
Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
arXiv Detail & Related papers (2024-07-26T06:29:09Z) - FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud [7.711666704468952]
We address the problem of traversability assessment using point clouds.
We propose a pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume.
We then propose a newtemporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds.
arXiv Detail & Related papers (2024-06-24T12:01:55Z) - Find n' Propagate: Open-Vocabulary 3D Object Detection in Urban Environments [67.83787474506073]
We tackle the limitations of current LiDAR-based 3D object detection systems.
We introduce a universal textscFind n' Propagate approach for 3D OV tasks.
We achieve up to a 3.97-fold increase in Average Precision (AP) for novel object classes.
arXiv Detail & Related papers (2024-03-20T12:51:30Z) - HEDNet: A Hierarchical Encoder-Decoder Network for 3D Object Detection
in Point Clouds [19.1921315424192]
3D object detection in point clouds is important for autonomous driving systems.
A primary challenge in 3D object detection stems from the sparse distribution of points within the 3D scene.
We propose HEDNet, a hierarchical encoder-decoder network for 3D object detection.
arXiv Detail & Related papers (2023-10-31T07:32:08Z) - UnLoc: A Universal Localization Method for Autonomous Vehicles using
LiDAR, Radar and/or Camera Input [51.150605800173366]
UnLoc is a novel unified neural modeling approach for localization with multi-sensor input in all weather conditions.
Our method is extensively evaluated on Oxford Radar RobotCar, ApolloSouthBay and Perth-WA datasets.
arXiv Detail & Related papers (2023-07-03T04:10:55Z) - Transformers for Object Detection in Large Point Clouds [9.287964414592826]
We present TransLPC, a novel detection model for large point clouds based on a transformer architecture.
We propose a novel query refinement technique to improve detection accuracy, while retaining a memory-friendly number of transformer decoder queries.
This simple technique has a significant effect on detection accuracy, which is evaluated on the challenging nuScenes dataset on real-world lidar data.
arXiv Detail & Related papers (2022-09-30T06:35:43Z) - AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
We propose a novel 3D detection framework that associates intact features for objects via domain adaptation.
We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
arXiv Detail & Related papers (2022-08-24T16:54:38Z) - M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object
Detection with Transformers [78.48081972698888]
We present M3DeTR, which combines different point cloud representations with different feature scales based on multi-scale feature pyramids.
M3DeTR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers.
arXiv Detail & Related papers (2021-04-24T06:48:23Z) - Stereo RGB and Deeper LIDAR Based Network for 3D Object Detection [40.34710686994996]
3D object detection has become an emerging task in autonomous driving scenarios.
Previous works process 3D point clouds using either projection-based or voxel-based models.
We propose the Stereo RGB and Deeper LIDAR framework which can utilize semantic and spatial information simultaneously.
arXiv Detail & Related papers (2020-06-09T11:19:24Z)
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.