lidar
PulseAugur coverage of lidar — every cluster mentioning lidar across labs, papers, and developer communities, ranked by signal.
- instance of SemanticKITTI 90%
- uses Camera 90%
- used by Camera 80%
- used by Nuscenes 70%
- used by alphaXiv 70%
- used by ScienceCast 70%
- used by three-dimensional object detection 70%
- used by Autonomous Vehicles 70%
- instance of Autonomous Vehicles 70%
- used by Cameras 70%
- used by Carla 70%
- used by 4D radar 70%
- 2026-05-18 research_milestone A new method for imaging hidden objects using consumer LiDAR was published. source
23 day(s) with sentiment data
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Machine learning models show improved path loss prediction for LPWANs
Researchers have conducted a systematic analysis of machine learning models for predicting path loss in Low Power Wide Area Networks (LPWANs), specifically focusing on LoRa technology. The study employed Random Forest m…
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New LiDAR sampling method improves 3D construction scene analysis
Researchers have developed a new incidence-aware sampling strategy for individual LiDAR scans to improve 3D scene understanding in construction. This method addresses challenges like limited surface coverage and varying…
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New SIP dataset targets 3D scene understanding in construction sites
Researchers have introduced SIP, a new dataset designed to improve 3D scene understanding in active construction sites. Unlike existing datasets that use uniformly sampled and complete scans, SIP captures the fragmented…
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New CRUISE framework enhances autonomous driving sensor fusion with VLM-guided uncertainty
Researchers have developed CRUISE, a new framework for autonomous driving that enhances sensor fusion by incorporating uncertainty quantification guided by a vision-language model (VLM). This approach aims to improve th…
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Vernata framework enhances LiDAR point cloud learning with self-supervision
Researchers have developed Vernata, a new self-supervised learning framework designed to improve deep learning models for LiDAR point clouds. This framework extends the Sonata architecture with sparse view augmentation,…
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New 3D perception workflow enables real-time LiDAR classification on Raspberry Pi 5
Researchers have developed a new workflow for 3D deep learning on edge devices, specifically optimized for the Raspberry Pi 5. This approach utilizes physics-based simulation to create a synthetic LiDAR dataset, address…
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FCC proposes ban on foreign LiDAR drones, citing military-grade tech
The U.S. Federal Communications Commission (FCC) is proposing a ban on foreign-made drones equipped with LiDAR technology, classifying it as "military-grade." This move, detailed in a public notice, aims to retroactivel…
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Ford Motor Company advances autonomous vehicle technology with AI and sustainability focus
Ford Motor Company is actively developing its autonomous vehicle technology, integrating artificial intelligence, machine learning, and advanced sensor systems. The company aims to lead the automotive industry's transit…
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New LiDAR framework enhances 3D place recognition for autonomous driving
Researchers have developed a new framework for 3D place recognition using LiDAR data, crucial for autonomous driving. The system employs an implicit 3D representation with elastic neural points to create fused descripto…
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LiDAR Scene Completion: Iterative Refinement vs. Wider Predictors Studied
A new research paper explores the effectiveness of test-time refinement strategies for LiDAR semantic scene completion. The study compares one-shot predictors, wider one-shot predictors, and iterative refinement systems…
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LoDA method advances 3D LiDAR change detection with new benchmark
Researchers have developed LoDA, a novel method for detecting object-level changes in 3D LiDAR maps, crucial for autonomous driving and smart-city services. This approach integrates detection-limit-aware registration an…
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Self-driving cars use AI and sensors, with 6 automation levels defined
Self-driving cars, also known as autonomous vehicles, use a combination of sensors like lidar, radar, and cameras, along with artificial intelligence and machine learning, to navigate and adjust their speed without huma…
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UQ-Loc method enhances LiDAR localization with uncertainty awareness
Researchers have developed UQ-Loc, a novel method for uncertainty-aware LiDAR scene coordinate regression. This approach extends the existing LightLoc architecture by predicting a full covariance matrix for each voxel, …
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New dataset and framework enhance 3D visual grounding for autonomous driving
Researchers have introduced Talk2Sensors, a novel dataset and framework for 3D visual grounding in autonomous driving that leverages multiple sensor modalities. The dataset includes over 8,000 language instructions and …
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Fusion-Poly framework enhances 3D multi-object tracking with asynchronous sensor fusion
Researchers have introduced Fusion-Poly, a novel framework designed to enhance 3D multi-object tracking by effectively integrating data from LiDAR and cameras. This system addresses the challenge of differing sensor sam…
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VLC Fusion framework uses VLM to improve sensor fusion for object detection
Researchers have developed VLC Fusion, a new framework designed to improve object detection by adaptively weighting sensor inputs based on environmental conditions. This approach utilizes a Vision-Language Model (VLM) t…
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Mamba-based knowledge distillation enhances LiDAR 3D object detection
Researchers have developed a new knowledge distillation framework to improve the efficiency of 3D object detection using LiDAR sensors. This method transfers object-level voxel representations from a powerful teacher mo…
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New TravKAN framework offers faster, interpretable robot navigation
Researchers have developed TravKAN, a new framework for traversability analysis in autonomous robots that utilizes Kolmogorov-Arnold Networks. This approach offers faster processing and greater interpretability compared…
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CalibBEV method aligns LiDAR-camera data for improved calibration
Researchers have introduced CalibBEV, a new method for calibrating LiDAR and camera sensors by aligning their Bird's Eye View (BEV) representations. This approach unifies sensor data into a shared 3D spatial representat…
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New research integrates LiDAR and camera for advanced video synthesis and motion capture
Two new research papers introduce advanced methods for integrating LiDAR and camera data in computer vision tasks. ReCamDriving focuses on synthesizing realistic videos for autonomous driving by using 3D Gaussian splatt…