STONE: Pioneering the One-to-N Universal Backdoor Threat in 3D Point Cloud

A new study introduces Semantic Orthogonal Calibration (SoC), a method aimed at improving the calibration of uncertainty estimates in vision-language models (VLMs) during test-time prompt tuning. This approach addresses the challenge of overconfidence in models by enforcing smooth prototype separation while maintaining semantic proximity.

A recent survey on learning-based Multi-View Stereo (MVS) techniques highlights the advancements in 3D reconstruction, which is crucial for applications such as Augmented and Virtual Reality, autonomous driving, and robotics. The study categorizes these methods into depth map-based, voxel-based, NeRF-based, and others, emphasizing the effectiveness of depth map-based approaches.

A recent study has introduced a framework aimed at mitigating hallucination issues in Multimodal Large Language Models (MLLMs) during Reinforcement Learning (RL) optimization. The research identifies key factors contributing to hallucinations, including over-reliance on visual reasoning and insufficient exploration diversity. The proposed framework incorporates modules for caption feedback, diversity-aware sampling, and conflict regularization to enhance model reliability.

A new framework named SPARK has been introduced for scalable real-time multi-camera point cloud aggregation, addressing challenges in 3D reconstruction, particularly in handling extrinsic uncertainty and multi-view fusion. This innovative approach combines geometry-aware online extrinsic estimation with a confidence-driven point cloud fusion strategy, enabling stable point cloud generation in dynamic environments.

A recent study on differentially private policy optimization (DPPO) has been published, focusing on the sample complexity of policy optimization (PO) in reinforcement learning (RL). This research addresses privacy concerns in sensitive applications such as robotics and healthcare by formalizing a definition of differential privacy tailored to PO and analyzing the sample complexity of various PO algorithms under DP constraints.

The landscape of video generation is evolving, transitioning from merely creating visually appealing clips to constructing interactive virtual environments that adhere to physical plausibility. This shift is highlighted in a recent survey that conceptualizes modern video foundation models as a combination of implicit world models and video renderers, enabling coherent visual reasoning and task planning.