Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Imperceptible 3D Point Cloud Attacks on Lattice-based Barycentric Coordinates
Authors: Keke Tang, Ziyong Du, Weilong Peng, Xiaofei Wang, Daizong Liu, Ligang Liu, Zhihong Tian
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate that integrating these local parametric constraints into conventional adversarial attacks yields superior imperceptibility, outperforming state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Guangzhou University 2University of Science and Technology of China 3Peking University |
| Pseudocode | No | The paper describes methods and formulations but does not contain a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We evaluate on Model Net40 (Wu et al. 2015) and Shape Net Part (Chang et al. 2015), sampling 1,024 points per cloud following (Xiang, Qi, and Li 2019). |
| Dataset Splits | No | The paper mentions "sampling 1,024 points per cloud" and using Model Net40 and Shape Net Part datasets, but does not explicitly provide details about training/test/validation splits (e.g., percentages, sample counts, or specific split files). |
| Hardware Specification | Yes | Experiments are conducted on a workstation with dual 2.40 GHz CPUs, 128 GB RAM, and eight NVIDIA RTX 3090 GPUs. |
| Software Dependencies | No | We implement the LBC-constrained adversarial attack framework using Py Torch (Paszke et al. 2019). While PyTorch is mentioned, a specific version number for the library itself is not provided. |
| Experiment Setup | Yes | Following (Gu et al. 2019), we construct a permutohedral lattice with a resolution of r = 10 along each axis. For lattice subdivision, we compute each cell s density score and mean curvature, normalize them to [0,1], and calculate the subdivision criterion S( ) as their weighted sum with λd = 0.5. The subdivision threshold τ is set so that the top one-third of cells are subdivided. During lattice refinement, we optimize vertex positions over 2,000 iterations, setting λa = 10.0 to maintain cell angles. |