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Found 22 papers, 17 papers with code
Fast and Accurate Deep Loop Closing and Relocalization for Reliable LiDAR SLAM
In the end, we integrate our LCR-Net into a SLAM system and achieve robust and accurate online LiDAR SLAM in outdoor driving environments.
TFNet: Exploiting Temporal Cues for Fast and Accurate LiDAR Semantic Segmentation
There are different types of methods, such as point-based, range-image-based, polar-based, and hybrid methods.
PowerBEV: A Powerful Yet Lightweight Framework for Instance Prediction in Bird's-Eye View
Accurately perceiving instances and predicting their future motion are key tasks for autonomous vehicles, enabling them to navigate safely in complex urban traffic.
RDMNet: Reliable Dense Matching Based Point Cloud Registration for Autonomous Driving
The proposed 3D-RoFormer fuses 3D position information into the transformer network, efficiently exploiting point clouds' contextual and geometric information to generate robust superpoint correspondences.
CCL: Continual Contrastive Learning for LiDAR Place Recognition
The experimental results show that our CCL consistently improves the performance of different methods in different environments outperforming the state-of-the-art continual learning method.
NeRF-LOAM: Neural Implicit Representation for Large-Scale Incremental LiDAR Odometry and Mapping
To bridge this gap, in this paper, we propose a novel NeRF-based LiDAR odometry and mapping approach, NeRF-LOAM, consisting of three modules neural odometry, neural mapping, and mesh reconstruction.
ElC-OIS: Ellipsoidal Clustering for Open-World Instance Segmentation on LiDAR Data
In this paper, we present a flexible and effective OIS framework for LiDAR point cloud that can accurately segment both known and unknown instances (i. e., seen and unseen instance categories during training).
InsMOS: Instance-Aware Moving Object Segmentation in LiDAR Data
We evaluated our approach on the LiDAR-MOS benchmark based on SemanticKITTI and achieved better moving object segmentation performance compared to state-of-the-art methods, demonstrating the effectiveness of our approach in integrating instance information for moving object segmentation.
CVTNet: A Cross-View Transformer Network for Place Recognition Using LiDAR Data
LiDAR-based place recognition (LPR) is one of the most crucial components of autonomous vehicles to identify previously visited places in GPS-denied environments.
Temporal Consistent 3D LiDAR Representation Learning for Semantic Perception in Autonomous Driving
Especially in autonomous driving, point clouds are sparse, and objects appearance depends on its distance from the sensor, making it harder to acquire large amounts of labeled training data.
SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation
However, current works are based on raw data or network feature-level fusion and only consider short-range HD map generation, limiting their deployment to realistic autonomous driving applications.
IR-MCL: Implicit Representation-Based Online Global Localization
The experiments suggest that the presented implicit representation is able to predict more accurate 2D LiDAR scans leading to an improved observation model for our particle filter-based localization.
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization.
SeqOT: A Spatial-Temporal Transformer Network for Place Recognition Using Sequential LiDAR Data
It uses multi-scale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion.
BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM
To address this limitation, we present a novel Bag of Words for real-time loop closing in 3D LiDAR SLAM, called BoW3D.
Online Pole Segmentation on Range Images for Long-term LiDAR Localization in Urban Environments
We further use the extracted poles as pseudo labels to train a deep neural network for online range image-based pole segmentation.
Efficient Spatial-Temporal Information Fusion for LiDAR-Based 3D Moving Object Segmentation
We also use a point refinement module via 3D sparse convolution to fuse the information from both LiDAR range image and point cloud representations and reduce the artifacts on the borders of the objects.
Receding Moving Object Segmentation in 3D LiDAR Data Using Sparse 4D Convolutions
A key challenge for autonomous vehicles is to navigate in unseen dynamic environments.
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for In-situ Segmentation of On-branch Soybean Pod
In the first step, we finetune an instance segmentation network pretrained by a source domain (MS COCO dataset) with a synthetic target domain (in-vitro soybean pods dataset).
Self-supervised Point Cloud Prediction Using 3D Spatio-temporal Convolutional Networks
In this paper, we address the problem of predicting future 3D LiDAR point clouds given a sequence of past LiDAR scans.
Multi-scale Interaction for Real-time LiDAR Data Segmentation on an Embedded Platform
Real-time semantic segmentation of LiDAR data is crucial for autonomously driving vehicles, which are usually equipped with an embedded platform and have limited computational resources.
Ranked #2 on
Real-Time 3D Semantic Segmentation
on SemanticKITTI
Extreme Low Resolution Activity Recognition with Confident Spatial-Temporal Attention Transfer
Given the fact that one same activity may be represented by videos in both high resolution (HR) and extreme low resolution (eLR), it is worth studying to utilize the relevant HR data to improve the eLR activity recognition.
