Yuecheng Wang

Yuecheng Wang, Jonathan Woodruff, Alfredo Mazzinghi et al.

In this paper, we present PoisonCap: scalable temporal safety with strict use-after-free protection and initialisation safety for CHERI systems. Efficient memory safety is an increasing priority for programming languages, operating systems, and hardware designs, and CHERI is a leading hardware/software system that provides native spatial safety and a foundation for temporal memory safety. Cornucopia Reloaded, the current state-of-the-art CHERI temporal safety solution, provides use-after-reallocation safety instead of stronger use-after-free safety, and is not able to enforce initialisation safety. We show that a new 'poison' capability format can be used to enforce strict use-after-free and initialisation safety, and also to communicate memory state to the microarchitecture for efficient cache management of quarantined memory. We enable elegant delegation of memory poisoning privilege using capability bounds to allow nested allocators to enforce safety on their consumers without disturbing upstream allocators. PoisonCap can replace the Cornucopia shadow bitmap, and also automatically zeros memory on reallocation, or optionally traps on read-before-write to enforce initialisation safety. As a result, it incurs no fundamental overhead relative to a Cornucopia baseline that zeros before reallocation, strengthening CHERI temporal safety without performance overhead.

Zikuan Li, Honghua Chen, Yuecheng Wang et al.

Extracting geometric edges from unstructured point clouds remains a significant challenge, particularly in thin-walled structures that are commonly found in everyday objects. Traditional geometric methods and recent learning-based approaches frequently struggle with these structures, as both rely heavily on sufficient contextual information from local point neighborhoods. However, 3D measurement data of thin-walled structures often lack the accurate, dense, and regular neighborhood sampling required for reliable edge extraction, resulting in degraded performance. In this work, we introduce STAR-Edge, a novel approach designed for detecting and refining edge points in thin-walled structures. Our method leverages a unique representation-the local spherical curve-to create structure-aware neighborhoods that emphasize co-planar points while reducing interference from close-by, non-co-planar surfaces. This representation is transformed into a rotation-invariant descriptor, which, combined with a lightweight multi-layer perceptron, enables robust edge point classification even in the presence of noise and sparse or irregular sampling. Besides, we also use the local spherical curve representation to estimate more precise normals and introduce an optimization function to project initially identified edge points exactly on the true edges. Experiments conducted on the ABC dataset and thin-walled structure-specific datasets demonstrate that STAR-Edge outperforms existing edge detection methods, showcasing better robustness under various challenging conditions.