One-to-N Backdoor Attack in 3D Point Cloud via Spherical Trigger

The article discusses the growing interest in world models, particularly in light of advancements in multimodal large language models like GPT-4 and video generation models such as Sora. World models serve two main purposes: understanding current world mechanisms and predicting future dynamics. The review categorizes these models and highlights their applications in various fields, including generative games, autonomous driving, robotics, and social simulacra, emphasizing their role in decision-making processes.

The article discusses a novel framework called Learning with Preserving (LwP) designed for Continual Multitask Learning (CMTL) in artificial intelligence systems. CMTL involves models that learn new tasks sequentially without forgetting previously acquired skills, which is crucial in fields like autonomous driving and medical imaging. Traditional methods often struggle due to task-specific feature fragmentation. LwP focuses on maintaining the geometric structure of shared representation spaces, enhancing the model's ability to learn continuously.

MMEdge is a new framework designed for real-time multimodal inference on resource-constrained edge devices, crucial for applications like autonomous driving and mobile health. The framework addresses the challenges of sensing dynamics and model execution by decomposing the inference process into fine-grained sensing and encoding units. This allows for incremental computation as data arrives, while a lightweight temporal aggregation module captures rich temporal dynamics to maintain accuracy.

The article discusses the introduction of SurfaceBench, a new benchmark for symbolic surface discovery in machine learning. This benchmark addresses the challenge of equation discovery from data, which is crucial for understanding complex physical and geometric phenomena. SurfaceBench includes 183 tasks across 15 categories of symbolic complexity, featuring various equation representation forms and synthetic three-dimensional data. It aims to improve upon existing benchmarks that often focus on scalar functions and rely on inadequate metrics.

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.

The article discusses advancements in autonomous driving systems that utilize 3D object detection through RGB cameras, which are more cost-effective than LiDAR. Despite their promising detection accuracy, these systems are vulnerable to adversarial attacks. The study introduces AdvRoad, a method to create realistic road-style adversarial posters that can deceive detection systems without being easily noticed. This approach aims to enhance the safety and reliability of autonomous driving technologies.

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 efficiency without sacrificing accuracy. Key innovations include a Quantization-Friendly LiDAR-ray Position Embedding and techniques to mitigate accuracy degradation typically associated with quantization methods.