lidar
PulseAugur coverage of lidar — every cluster mentioning lidar across labs, papers, and developer communities, ranked by signal.
- 2026-05-18 research_milestone A new method for imaging hidden objects using consumer LiDAR was published. source
18 day(s) with sentiment data
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UniFlow model advances LiDAR scene flow for autonomous vehicles
Researchers have developed UniFlow, a novel feedforward model designed to improve LiDAR scene flow estimation for autonomous vehicles. Unlike previous methods that performed best when trained on a single dataset, UniFlo…
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AI framework estimates urban tree biomass using LiDAR and optical data
Researchers have developed a new framework for estimating above-ground biomass (AGB) of individual trees in urban environments using airborne LiDAR and optical imagery. This method, applied to an 810 km² area in Ontario…
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New unsupervised method enhances obstacle detection for agricultural robots
Researchers have developed a new unsupervised anomaly detection method called Video Memory Transformers for Anomaly Detection (VMTAD) specifically for autonomous agricultural rovers. This transformer-based system uses a…
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New DSP-SLAM++ framework enhances real-time object SLAM capabilities
Researchers have introduced DSP-SLAM++, a unified framework designed to improve object-aware Simultaneous Localization and Mapping (SLAM) systems. This new framework addresses the trade-offs between real-time performanc…
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New benchmark improves 3D object detection for cyclists using auto-labels
Researchers have developed a new method for improving 3D object detection for autonomous driving systems, specifically focusing on vulnerable road users (VRUs) from a cyclist's perspective. The study introduces a benchm…
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New SkyLume Dataset Tackles 3D Urban Reconstruction Under Varying Light
Researchers have introduced SkyLume, a large-scale aerial dataset designed to address challenges in 3D urban scene reconstruction under varying illumination conditions. The dataset comprises over 100,000 high-resolution…
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MM-TRELLIS generates 3D vehicles using multi-modal sensor data · 2 sources tracked
Researchers have developed MM-TRELLIS, a novel method for generating realistic 3D vehicle models from autonomous driving data. This approach integrates multi-view images and LiDAR point clouds into native 3D generative …
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AI framework OmniPath audits wheelchair accessibility using LiDAR and OSM data
Researchers have developed OmniPath, a multi-modal agentic framework designed to audit wheelchair accessibility by analyzing pedestrian environments. The system integrates OpenStreetMap data with high-density aerial LiD…
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Naver Labs Europe unveils DIVINE encoder for autonomous robots
Naver Labs Europe has introduced DIVINE, a versatile encoder designed for autonomous robots. This system aims to enhance robot perception and navigation capabilities by processing various sensor inputs. DIVINE is intend…
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Lyft mandates multi-sensor approach for autonomous vehicles on its platform
Lyft is establishing a new safety standard for autonomous vehicles (AVs) that will operate on its platform, requiring a multi-sensor approach. The company believes that relying on a single type of sensor, such as camera…
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UECP framework enhances autonomous driving perception with uncertainty mapping
Researchers have introduced UECP, a new framework for enhancing collaborative perception in autonomous driving. UECP utilizes an uncertainty map, derived from real-time LiDAR data, to provide an unbiased metric for weig…
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DrivingVoxels framework enhances dynamic scene reconstruction
Researchers have introduced DrivingVoxels, a new framework designed to improve the reconstruction of dynamic driving scenes. This method addresses limitations in existing approaches, such as the time-consuming nature of…
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New ShotcreteDepth dataset combines RGB and LiDAR for robotic depth perception
A new bi-modal dataset called ShotcreteDepth has been released, combining stereo RGB and LiDAR data for robotic depth perception in construction environments. This dataset is designed to address challenges posed by hars…
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New XAI dataset and method enhance species distribution model interpretability
Researchers have introduced a novel approach to enhance the interpretability of complex deep learning models used for species distribution modeling (SDMs). This method employs concept-based Explainable AI (XAI) techniqu…
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New PCFootprint dataset advances building footprint extraction from LiDAR
Researchers have introduced PCFootprint, a new large-scale dataset designed for extracting vectorized building footprints from aerial LiDAR point clouds. This dataset, comprising 33,000 tiles derived from the Estonian L…
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HilDA framework advances self-supervised LiDAR pre-training for autonomous driving
Researchers have introduced HilDA, a novel self-supervised pretraining framework designed to enhance LiDAR backbones for autonomous driving applications. This framework leverages Vision Foundation Models (VFMs) for hier…
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New PC-TGS framework enhances wireless channel prediction using LiDAR and radio data
Researchers have developed a new framework called Point-Cloud-Assisted Tangent Gaussian Splatting (PC-TGS) to improve channel prediction in wireless networks. This method integrates sparse radio measurements with dense …
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New SSIL Framework Enables Self-Supervised End-to-End Driving
Researchers have introduced Self-Supervised Imitation Learning (SSIL), a novel framework for end-to-end autonomous driving that does not require labeled driving commands or pre-trained models. SSIL generates pseudo stee…
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New HRDX dataset advances autonomous driving HD map construction
Researchers have introduced HRDX, a new large-scale dataset for constructing vector HD maps crucial for autonomous driving. Spanning approximately 1,400 km of driving data, HRDX is significantly larger than existing pub…
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LiDAR place recognition framework improves aerial-ground data matching
Researchers have developed a novel framework for aerial-ground LiDAR place recognition, addressing challenges like the domain gap and false positives. Their approach utilizes patch-level self-supervised learning to enha…