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Found 19 papers, 8 papers with code
Stereo-LiDAR Depth Estimation with Deformable Propagation and Learned Disparity-Depth Conversion
To address this issue, we propose a novel stereo-LiDAR depth estimation network with Semi-Dense hint Guidance, named SDG-Depth.
Thermal-NeRF: Neural Radiance Fields from an Infrared Camera
In recent years, Neural Radiance Fields (NeRFs) have demonstrated significant potential in encoding highly-detailed 3D geometry and environmental appearance, positioning themselves as a promising alternative to traditional explicit representation for 3D scene reconstruction.
Toward Open Vocabulary Aerial Object Detection with CLIP-Activated Student-Teacher Learning
In this paper, we aim to develop open-vocabulary object detection (OVD) technique in aerial images that scales up object vocabulary size beyond training data.
Timestamp-supervised Wearable-based Activity Segmentation and Recognition with Contrastive Learning and Order-Preserving Optimal Transport
To address these challenges, we propose a novel method for joint activity segmentation and recognition with timestamp supervision, in which only a single annotated sample is needed in each activity segment.
TextSLAM: Visual SLAM with Semantic Planar Text Features
The results show that integrating texture features leads to a more superior SLAM system that can match images across day and night.
Sparsity-Aware Optimal Transport for Unsupervised Restoration Learning
Recent studies show that, without any prior model, the unsupervised restoration learning problem can be optimally formulated as an optimal transport (OT) problem, which has shown promising performance on denoising tasks to approach the performance of supervised methods.
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.
FeatureBooster: Boosting Feature Descriptors with a Lightweight Neural Network
We introduce a lightweight network to improve descriptors of keypoints within the same image.
Multi-level Contrast Network for Wearables-based Joint Activity Segmentation and Recognition
Human activity recognition (HAR) with wearables is promising research that can be widely adopted in many smart healthcare applications.
Explainable Analysis of Deep Learning Methods for SAR Image Classification
Deep learning methods exhibit outstanding performance in synthetic aperture radar (SAR) image interpretation tasks.
M2DGR: A Multi-sensor and Multi-scenario SLAM Dataset for Ground Robots
We introduce M2DGR: a novel large-scale dataset collected by a ground robot with a full sensor-suite including six fish-eye and one sky-pointing RGB cameras, an infrared camera, an event camera, a Visual-Inertial Sensor (VI-sensor), an inertial measurement unit (IMU), a LiDAR, a consumer-grade Global Navigation Satellite System (GNSS) receiver and a GNSS-IMU navigation system with real-time kinematic (RTK) signals.
StructDepth: Leveraging the structural regularities for self-supervised indoor depth estimation
Inspired by the early works on indoor modeling, we leverage the structural regularities exhibited in indoor scenes, to train a better depth network.
A Pose-only Solution to Visual Reconstruction and Navigation
Visual navigation and three-dimensional (3D) scene reconstruction are essential for robotics to interact with the surrounding environment.
MARS: Mixed Virtual and Real Wearable Sensors for Human Activity Recognition with Multi-Domain Deep Learning Model
Together with the rapid development of the Internet of Things (IoT), human activity recognition (HAR) using wearable Inertial Measurement Units (IMUs) becomes a promising technology for many research areas.
Attention-SLAM: A Visual Monocular SLAM Learning from Human Gaze
This paper proposes a novel simultaneous localization and mapping (SLAM) approach, namely Attention-SLAM, which simulates human navigation mode by combining a visual saliency model (SalNavNet) with traditional monocular visual SLAM.
A Deep Learning Method for Complex Human Activity Recognition Using Virtual Wearable Sensors
The experimental results show that the proposed method can surprisingly converge in a few iterations and achieve an accuracy of 91. 15% on a real IMU dataset, demonstrating the efficiency and effectiveness of the proposed method.
TextSLAM: Visual SLAM with Planar Text Features
We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline.
General Method for Prime-point Cyclic Convolution over the Real Field
A general and fast method is conceived for computing the cyclic convolution of n points, where n is a prime number.
StructVIO : Visual-inertial Odometry with Structural Regularity of Man-made Environments
Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity.
Robotics
