World PulseNow
Newsletter

Urban 3D Change Detection Using LiDAR Sensor for HD Map Maintenance and Smart Mobility

arXiv — cs.CV•Monday, October 27, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
The recent advancements in urban 3D change detection using LiDAR sensors are set to revolutionize HD map maintenance and smart mobility solutions. This technology enhances the accuracy of object-level change detection, which is crucial for applications like autonomous driving and digital twins. By overcoming the limitations of traditional methods, this innovation promises to improve construction monitoring and localization, making our cities smarter and safer. It's an exciting development that could significantly impact how we navigate and interact with urban environments.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended Readings
Scaling innovation in manufacturing with AI
MIT Technology Review8 hours ago
Scaling innovation in manufacturing with AI
PositiveArtificial Intelligence
Manufacturing is undergoing a significant transformation as artificial intelligence (AI) enhances existing technologies such as digital twins, cloud computing, edge computing, and the industrial internet of things (IIoT). This shift allows factory operations teams to move from reactive problem-solving to proactive optimization across systems, improving efficiency and productivity.
Read full article
via MIT Technology Review
Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM
arXiv — cs.CV20 hours ago
Enhancing End-to-End Autonomous Driving with Risk Semantic Distillaion from VLM
PositiveArtificial Intelligence
The paper introduces Risk Semantic Distillation (RSD), a novel framework aimed at enhancing end-to-end autonomous driving (AD) systems. While current AD systems perform well in complex scenarios, they struggle with generalization to unseen situations. RSD leverages Vision-Language Models (VLMs) to improve training efficiency and consistency in trajectory planning, addressing challenges posed by hybrid AD systems that utilize multiple planning approaches. This advancement is crucial for the future of autonomous driving technology.
Read full article
via arXiv — cs.CV
STONE: Pioneering the One-to-N Backdoor Threat in 3D Point Cloud
arXiv — cs.CV20 hours ago
STONE: Pioneering the One-to-N Backdoor Threat in 3D Point Cloud
PositiveArtificial Intelligence
Backdoor attacks represent a significant risk to deep learning, particularly in critical 3D applications like autonomous driving and robotics. Current methods primarily focus on static one-to-one attacks, leaving the more versatile one-to-N backdoor threat largely unaddressed. The introduction of STONE (Spherical Trigger One-to-N Backdoor Enabling) marks a pivotal advancement, offering a configurable spherical trigger that can manipulate multiple output labels while maintaining high accuracy in clean data.
Read full article
via arXiv — cs.CV
Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
arXiv — cs.CV20 hours ago
Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving
NeutralArtificial Intelligence
A recent study has introduced a novel physical adversarial attack targeting stereo matching models used in autonomous driving. Unlike traditional attacks that utilize 2D patches, this method employs a 3D physical adversarial example (PAE) with global camouflage texture, enhancing visual consistency across various viewpoints of stereo cameras. The research also presents a new 3D stereo matching rendering module to align the PAE with real-world positions, addressing the disparity effects inherent in binocular vision.
Read full article
via arXiv — cs.CV
VLMs Guided Interpretable Decision Making for Autonomous Driving
arXiv — cs.CV20 hours ago
VLMs Guided Interpretable Decision Making for Autonomous Driving
PositiveArtificial Intelligence
Recent advancements in autonomous driving have investigated the application of vision-language models (VLMs) in visual question answering (VQA) frameworks for driving decision-making. However, these methods often rely on handcrafted prompts and exhibit inconsistent performance, which hampers their effectiveness in real-world scenarios. This study assesses state-of-the-art open-source VLMs on high-level decision-making tasks using ego-view visual inputs, revealing significant limitations in their ability to provide reliable, context-aware decisions.
Read full article
via arXiv — cs.CV
Understanding World or Predicting Future? A Comprehensive Survey of World Models
arXiv — cs.LG2 days ago
Understanding World or Predicting Future? A Comprehensive Survey of World Models
NeutralArtificial Intelligence
The article discusses the growing interest in world models, particularly in the context of advancements in multimodal large language models like GPT-4 and video generation models such as Sora. It provides a comprehensive review of the literature on world models, which serve to either understand the current state of the world or predict future dynamics. The review categorizes world models based on their functions: constructing internal representations and predicting future states, with applications in generative games, autonomous driving, robotics, and social simulacra.
Read full article
via arXiv — cs.LG
FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection
arXiv — cs.CV3 days ago
FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection
PositiveArtificial Intelligence
The paper titled 'FQ-PETR: Fully Quantized Position Embedding Transformation for Multi-View 3D Object Detection' addresses the challenges of deploying PETR models in autonomous driving due to their high computational costs and memory requirements. It introduces FQ-PETR, a fully quantized framework that aims to enhance performance while maintaining accuracy. The proposed innovations include a Quantization-Friendly LiDAR-ray Position Embedding and improvements in quantizing non-linear operators, which are critical for effective multi-view 3D detection.
Read full article
via arXiv — cs.CV
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
arXiv — cs.CV3 days ago
CATS-V2V: A Real-World Vehicle-to-Vehicle Cooperative Perception Dataset with Complex Adverse Traffic Scenarios
PositiveArtificial Intelligence
The CATS-V2V dataset introduces a pioneering real-world collection for Vehicle-to-Vehicle (V2V) cooperative perception, aimed at enhancing autonomous driving in complex adverse traffic scenarios. Collected using two time-synchronized vehicles, the dataset encompasses 100 clips featuring 60,000 frames of LiDAR point clouds and 1.26 million multi-view camera images across various weather and lighting conditions. This dataset is expected to significantly benefit the autonomous driving community by providing high-quality data for improved perception capabilities.
Read full article
via arXiv — cs.CV