D3DLO: Deep 3D LiDAR Odometry

• URL: http://arxiv.org/abs/2101.12242v1
• Date: Thu, 28 Jan 2021 19:23:06 GMT
• Title: D3DLO: Deep 3D LiDAR Odometry
• Abstract summary: LiDAR odometry (LO) describes the task of finding an alignment of subsequent LiDAR point clouds. This alignment can be used to estimate the motion of the platform where the LiDAR sensor is mounted on. We propose a network architecture that learns LO by directly processing 3D point clouds.
• Score: 1.1172382217477126
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: LiDAR odometry (LO) describes the task of finding an alignment of subsequent LiDAR point clouds. This alignment can be used to estimate the motion of the platform where the LiDAR sensor is mounted on. Currently, on the well-known KITTI Vision Benchmark Suite state-of-the-art algorithms are non-learning approaches. We propose a network architecture that learns LO by directly processing 3D point clouds. It is trained on the KITTI dataset in an end-to-end manner without the necessity of pre-defining corresponding pairs of points. An evaluation on the KITTI Vision Benchmark Suite shows similar performance to a previously published work, DeepCLR [1], even though our model uses only around 3.56% of the number of network parameters thereof. Furthermore, a plane point extraction is applied which leads to a marginal performance decrease while simultaneously reducing the input size by up to 50%.

Related papers

• Point-Plane Projections for Accurate LiDAR Semantic Segmentation in Small Data Scenarios [5.856790488516224]
  LiDAR point cloud semantic segmentation is essential for interpreting 3D environments in applications such as autonomous driving and robotics.<n>Recent methods achieve strong performance by exploiting different point cloud representations or incorporating data from other sensors, such as cameras or external datasets.<n>We improve the performance of point-based methods by effectively learning features from 2D representations through point-plane projections.
  arXiv  Detail & Related papers  (2025-09-13T15:03:12Z)
• V-DETR: DETR with Vertex Relative Position Encoding for 3D Object Detection [73.37781484123536]
  We introduce a highly performant 3D object detector for point clouds using the DETR framework. To address the limitation, we introduce a novel 3D Relative Position (3DV-RPE) method. We show exceptional results on the challenging ScanNetV2 benchmark.
  arXiv  Detail & Related papers  (2023-08-08T17:14:14Z)
• Clustering based Point Cloud Representation Learning for 3D Analysis [80.88995099442374]
  We propose a clustering based supervised learning scheme for point cloud analysis. Unlike current de-facto, scene-wise training paradigm, our algorithm conducts within-class clustering on the point embedding space. Our algorithm shows notable improvements on famous point cloud segmentation datasets.
  arXiv  Detail & Related papers  (2023-07-27T03:42:12Z)
• PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds [29.15589024703907]
  In this paper, we revisit the local point aggregators from the perspective of allocating computational resources. We find that the simplest pillar based models perform surprisingly well considering both accuracy and latency. Our results challenge the common intuition that the detailed geometry modeling is essential to achieve high performance for 3D object detection.
  arXiv  Detail & Related papers  (2023-05-08T17:59:14Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• Efficient 3D Deep LiDAR Odometry [16.388259779644553]
  An efficient 3D point cloud learning architecture, named PWCLO-Net, is first proposed in this paper. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask.
  arXiv  Detail & Related papers  (2021-11-03T11:09:49Z)
• 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)
• Multi Projection Fusion for Real-time Semantic Segmentation of 3D LiDAR Point Clouds [2.924868086534434]
  This paper introduces a novel approach for 3D point cloud semantic segmentation that exploits multiple projections of the point cloud. Our Multi-Projection Fusion framework analyzes spherical and bird's-eye view projections using two separate highly-efficient 2D fully convolutional models.
  arXiv  Detail & Related papers  (2020-11-03T19:40:43Z)
• SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks [81.64530401885476]
  We propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay.
  arXiv  Detail & Related papers  (2020-10-19T09:23:39Z)
• LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation [22.664095688406412]
  We propose to transfer the LiDAR frames to image space and reformulate the problem as image feature extraction. With the help of scale-invariant feature transform (SIFT) for feature extraction, we are able to generate matched keypoint pairs (MKPs) A convolutional neural network pipeline is designed for LiDAR odometry estimation by extracted MKPs. The proposed scheme, namely LodoNet, is then evaluated in the KITTI odometry estimation benchmark, achieving on par with or even better results than the state-of-the-art.
  arXiv  Detail & Related papers  (2020-09-01T01:09:41Z)
• Local Grid Rendering Networks for 3D Object Detection in Point Clouds [98.02655863113154]
  CNNs are powerful but it would be computationally costly to directly apply convolutions on point data after voxelizing the entire point clouds to a dense regular 3D grid. We propose a novel and principled Local Grid Rendering (LGR) operation to render the small neighborhood of a subset of input points into a low-resolution 3D grid independently. We validate LGR-Net for 3D object detection on the challenging ScanNet and SUN RGB-D datasets.
  arXiv  Detail & Related papers  (2020-07-04T13:57:43Z)
• Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study [2.6205925938720833]
  State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to use two-dimensional, image-like projections. We demonstrate various techniques to boost the performance and to improve runtime as well as memory constraints.
  arXiv  Detail & Related papers  (2020-04-06T11:08:12Z)

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.