arXiv subjects

Mingkai Li

Publications and source records attributed to Mingkai Li.

4 recordsLinked to original sources

arXiv2026

Industrial3D: A Water-Treatment TLS Point Cloud Dataset and Cross-Paradigm Benchmark for MEP Scene Understanding

Automated semantic understanding of dense terrestrial laser scanning (TLS) point clouds is a prerequisite for Scan-to-BIM, digital twin maintenance, and as-built verifcation. Yet for operational industrial mechanical, electrical, and plumbing (MEP) facilities, this challenge remains largely unsolved: water-treatment TLS scans exhibit extreme geometric ambiguity, severe occlusion, and extreme class imbalance that architectural benchmarks such as S3DIS and ScanNet cannot adequately represent. We present Industrial3D, a terrestrial LiDAR dataset with 612.7 million expert-labeled points at 6 mm resolution from 20 room scenes, 13 dataset areas, and 7 operational water treatment facilities. At 6.6x the scale of the closest comparable MEP dataset, Industrial3D provides the largest industrial MEP testbed for within-domain scene understanding. We further establish a cross-paradigm benchmark of nine methods across fully supervised, weakly supervised, unsupervised, and foundation-model settings. The best supervised method reaches 55.74% mIoU, whereas zero-shot Point-SAM reaches 15.79%, a 39.95 percentage-point gap that quantifes unresolved domain transfer for industrial TLS data. Analysis attributes this gap to a dual crisis: 215:1 statistical rarity and cylindrical geometric ambiguity between tail classes and head-class pipes. The dataset, benchmark code, and pre-trained models will be publicly released at https://github.com/pointcloudyc/Industrial3D.

cs.CV↗

arXiv2026

Challenges and Design Considerations for Finding CUDA Bugs Through GPU-Native Fuzzing

Modern computing is shifting from homogeneous CPU-centric systems to heterogeneous systems with closely integrated CPUs and GPUs. While the CPU software stack has benefited from decades of memory safety hardening, the GPU software stack remains dangerously immature. This discrepancy presents a critical ethical challenge: the world's most advanced AI and scientific workloads are increasingly deployed on vulnerable hardware components. In this paper, we study the key challenges of ensuring memory safety on heterogeneous systems. We show that, while the number of exploitable bugs in heterogeneous systems rises every year, current mitigation methods often rely on unfaithful translations, i.e., converting GPU programs to run on CPUs for testing, which fails to capture the architectural differences between CPUs and GPUs. We argue that the faithfulness of the program behavior is at the core of secure and reliable heterogeneous systems design. To ensure faithfulness, we discuss several design considerations of a GPU-native fuzzing pipeline for CUDA programs.

cs.CR↗

arXiv2026

SoPE: Spherical Coordinate-Based Positional Embedding for Enhancing Spatial Perception of 3D LVLMs

3D Large Vision-Language Models (3D LVLMs) built upon Large Language Models (LLMs) have achieved remarkable progress across various multimodal tasks. However, their inherited position-dependent modeling mechanism, Rotary Position Embedding (RoPE), remains suboptimal for 3D multimodal understanding. The vanilla RoPE formulation fails to preserve essential three-dimensional spatial structures when encoding 3D tokens, and its relative distance computation overlooks angular dependencies, hindering the model's ability to capture directional variations in visual representations. To overcome these limitations, we introduce Spherical Coordinate-based Positional Embedding (SoPE). Our method maps point-cloud token indices into a 3D spherical coordinate space, enabling unified modeling of spatial locations and directional angles. This formulation preserves the inherent geometric structure of point-cloud data, enhances spatial awareness, and yields more consistent and expressive geometric representations for multimodal learning. In addition, we introduce a multi-scale frequency mixing strategy to fuse feature information across different frequency domains. Experimental results on multiple 3D scene benchmarks validate the effectiveness of our approach, while real-world deployment experiments further demonstrate its strong generalization capability.

cs.CV↗

arXiv2025

NanoTag: Systems Support for Efficient Byte-Granular Overflow Detection on ARM MTE

Memory safety bugs, such as buffer overflows and use-after-frees, are the leading causes of software safety issues in production. Software-based approaches, e.g., Address Sanitizer (ASAN), can detect such bugs with high precision, but with prohibitively high overhead. ARM's Memory Tagging Extension (MTE) offers a promising alternative to detect these bugs in hardware with a much lower overhead. In this paper, we perform a thorough investigation of the first production implementation of ARM MTE (Google Pixel 8) and observe that MTE can only achieve coarse precision in bug detection compared with software-based approaches such as ASAN, mainly due to its 16-byte tag granularity. To address this issue, we present NANOTAG, a system to probabilistically detect buffer overflows at byte granularity in unmodified MTE-enabled binaries with minimal changes to memory allocators, introducing an explicit detection-performance tradeoff for in-house testing. NANOTAG detects buffer overflows at byte granularity by setting up a tripwire for tag granules that may require intra-granule overflow detection. The memory access to the tripwire causes additional overflow detection in the software while using MTE's hardware to detect bugs for the rest of the accesses. We implement NANOTAG based on the Scudo Hardened Allocator, the default memory allocator on Android since Android 11. Our evaluation results across popular benchmarks and real-world case studies show that NANOTAG detects nearly as many memory safety bugs as ASAN while incurring similar run-time overhead to Scudo Hardened Allocator in MTE SYNC mode.

cs.CR↗