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Found 24 papers, 15 papers with code
HS-SLAM: Hybrid Representation with Structural Supervision for Improved Dense SLAM
NeRF-based SLAM has recently achieved promising results in tracking and reconstruction.
A Text-Based Knowledge-Embedded Soft Sensing Modeling Approach for General Industrial Process Tasks Based on Large Language Model
To address these challenges, we propose a general framework named LLM-TKESS (large language model for text-based knowledge-embedded soft sensing), harnessing the powerful general problem-solving capabilities, cross-modal knowledge transfer abilities, and few-shot capabilities of LLM for enhanced soft sensing modeling.
A Soft Sensor Method with Uncertainty-Awareness and Self-Explanation Based on Large Language Models Enhanced by Domain Knowledge Retrieval
To achieve this, we propose a novel framework called the Few-shot Uncertainty-aware and self-Explaining Soft Sensor (LLM-FUESS), which includes the Zero-shot Auxiliary Variable Selector (LLM-ZAVS) and the Uncertainty-aware Few-shot Soft Sensor (LLM-UFSS).
Stereo Anything: Unifying Stereo Matching with Large-Scale Mixed Data
To further enrich the model's ability to generalize across different conditions, we introduce a novel synthetic dataset that complements existing data by adding variability in baselines, camera angles, and scene types.
LightStereo: Channel Boost Is All Your Need for Efficient 2D Cost Aggregation
We present LightStereo, a cutting-edge stereo-matching network crafted to accelerate the matching process.
Learning Temporally Consistent Video Depth from Video Diffusion Priors
Moreover, we design an effective training strategy to provide context within a clip.
A Tutorial on Gaussian Process Learning-based Model Predictive Control
This tutorial provides a systematic introduction to Gaussian process learning-based model predictive control (GP-MPC), an advanced approach integrating Gaussian process (GP) with model predictive control (MPC) for enhanced control in complex systems.
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth.
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments.
A Causal Disentangled Multi-Granularity Graph Classification Method
Graph data widely exists in real life, with large amounts of data and complex structures.
GO-SLAM: Global Optimization for Consistent 3D Instant Reconstruction
Neural implicit representations have recently demonstrated compelling results on dense Simultaneous Localization And Mapping (SLAM) but suffer from the accumulation of errors in camera tracking and distortion in the reconstruction.
Fault Separation Based on An Excitation Operator with Application to a Quadrotor UAV
This paper presents an excitation operator based fault separation architecture for a quadrotor unmanned aerial vehicle (UAV) subject to loss of effectiveness (LoE) faults, actuator aging, and load uncertainty.
Depth Super-Resolution from Explicit and Implicit High-Frequency Features
We propose a novel multi-stage depth super-resolution network, which progressively reconstructs high-resolution depth maps from explicit and implicit high-frequency features.
WL-Align: Weisfeiler-Lehman Relabeling for Aligning Users across Networks via Regularized Representation Learning
Aligning users across networks using graph representation learning has been found effective where the alignment is accomplished in a low-dimensional embedding space.
TemporalStereo: Efficient Spatial-Temporal Stereo Matching Network
We present TemporalStereo, a coarse-to-fine stereo matching network that is highly efficient, and able to effectively exploit the past geometry and context information to boost matching accuracy.
The Monocular Depth Estimation Challenge
This challenge evaluated the progress of self-supervised monocular depth estimation on the challenging SYNS-Patches dataset.
MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer
Self-supervised monocular depth estimation is an attractive solution that does not require hard-to-source depth labels for training.
Ranked #1 on
Monocular Depth Estimation
on KITTI
A Real-time Fire Segmentation Method Based on A Deep Learning Approach
Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy.
Ranked #1 on
Real-Time Semantic Segmentation
on FLAME
Autonomous Driving on Curvy Roads Without Reliance on Frenet Frame: A Cartesian-Based Trajectory Planning Method
This paper is focused on the trajectory planning task for autonomous driving on a curvy road.
Towards Improving Embedding Based Models of Social Network Alignment via Pseudo Anchors
Social network alignment aims at aligning person identities across social networks.
Auto-Detection of Tibial Plateau Angle in Canine Radiographs Using a Deep Learning Approach
Stifle joint issues are a major cause of lameness in dogs and it can be a significant marker for various forms of diseases or injuries.
Lightweight Combinational Machine Learning Algorithm for Sorting Canine Torso Radiographs
The veterinary field lacks automation in contrast to the tremendous technological advances made in the human medical field.
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects
To address this problem, we introduce a lightweight but effective Global Matching Component (GMC) to grab global matching features.
Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching
However, disparity is just a byproduct of a matching process modeled by cost volume, while indirectly learning cost volume driven by disparity regression is prone to overfitting since the cost volume is under constrained.
