arXiv subjects

Shenglin Wang

Publications and source records attributed to Shenglin Wang.

8 recordsLinked to original sources

arXiv2026

SpatialQ: Understanding 3D Gaussian Splatting Scene Quality via Visual-based MLLM

3D Gaussian Splatting (3DGS) has emerged as an effective representation for novel view synthesis and 3D scene reconstruction, creating an increasing demand for reliable quality assessment. Unlike conventional image quality assessment (IQA), the quality of a 3DGS scene depends not only on the perceptual fidelity of rendered views, but also on scene-level factors such as spatial structure and cross-view consistency. Existing IQA methods are limited by their reliance on 2D perceptual cues, whereas general multimodal large language models (MLLMs) are not designed for stable quality regression and may produce unreliable judgments. To address these limitations, a multimodal quality assessment framework is developed for 3DGS scene understanding. First, a 3D-aware quality representation learning framework is introduced by augmenting a VGGT-based encoder with a dedicated quality head. Multi-view images are encoded into view-specific features and aggregated to capture cross-view consistency, while geometric cues are incorporated through joint modeling of depth and point-cloud-related structural information, enabling the learning of structure-aware quality representations beyond appearance-driven features. Second, a grounded multimodal reasoning mechanism is constructed by jointly feeding original images, depth maps, point cloud renderings, and camera parameters into a Qwen-based MLLM.

cs.CV↗

arXiv2026

CreativeGame:Toward Mechanic-Aware Creative Game Generation

Large language models can generate plausible game code, but turning this capability into \emph{iterative creative improvement} remains difficult. In practice, single-shot generation often produces brittle runtime behavior, weak accumulation of experience across versions, and creativity scores that are too subjective to serve as reliable optimization signals. A further limitation is that mechanics are frequently treated only as post-hoc descriptions, rather than as explicit objects that can be planned, tracked, preserved, and evaluated during generation. This report presents \textbf{CreativeGame}, a multi-agent system for iterative HTML5 game generation that addresses these issues through four coupled ideas: a proxy reward centered on programmatic signals rather than pure LLM judgment; lineage-scoped memory for cross-version experience accumulation; runtime validation integrated into both repair and reward; and a mechanic-guided planning loop in which retrieved mechanic knowledge is converted into an explicit mechanic plan before code generation begins. The goal is not merely to produce a playable artifact in one step, but to support interpretable version-to-version evolution. The current system contains 71 stored lineages, 88 saved nodes, and a 774-entry global mechanic archive, implemented in 6{,}181 lines of Python together with inspection and visualization tooling. The system is therefore substantial enough to support architectural analysis, reward inspection, and real lineage-level case studies rather than only prompt-level demos. A real 4-generation lineage shows that mechanic-level innovation can emerge in later versions and can be inspected directly through version-to-version records. The central contribution is therefore not only game generation, but a concrete pipeline for observing progressive evolution through explicit mechanic change.

cs.AI↗

arXiv2025

GraspView: Active Perception Scoring and Best-View Optimization for Robotic Grasping in Cluttered Environments

Robotic grasping is a fundamental capability for autonomous manipulation, yet remains highly challenging in cluttered environments where occlusion, poor perception quality, and inconsistent 3D reconstructions often lead to unstable or failed grasps. Conventional pipelines have widely relied on RGB-D cameras to provide geometric information, which fail on transparent or glossy objects and degrade at close range. We present GraspView, an RGB-only robotic grasping pipeline that achieves accurate manipulation in cluttered environments without depth sensors. Our framework integrates three key components: (i) global perception scene reconstruction, which provides locally consistent, up-to-scale geometry from a single RGB view and fuses multi-view projections into a coherent global 3D scene; (ii) a render-and-score active perception strategy, which dynamically selects next-best-views to reveal occluded regions; and (iii) an online metric alignment module that calibrates VGGT predictions against robot kinematics to ensure physical scale consistency. Building on these tailor-designed modules, GraspView performs best-view global grasping, fusing multi-view reconstructions and leveraging GraspNet for robust execution. Experiments on diverse tabletop objects demonstrate that GraspView significantly outperforms both RGB-D and single-view RGB baselines, especially under heavy occlusion, near-field sensing, and with transparent objects. These results highlight GraspView as a practical and versatile alternative to RGB-D pipelines, enabling reliable grasping in unstructured real-world environments.

cs.RO↗

arXiv2025

Detecting the Root Cause Code Lines in Bug-Fixing Commits by Heterogeneous Graph Learning

With the continuous growth in the scale and complexity of software systems, defect remediation has become increasingly difficult and costly. Automated defect prediction tools can proactively identify software changes prone to defects within software projects, thereby enhancing software development efficiency. However, existing work in heterogeneous and complex software projects continues to face challenges, such as struggling with heterogeneous commit structures and ignoring cross-line dependencies in code changes, which ultimately reduce the accuracy of defect identification. To address these challenges, we propose an approach called RC_Detector. RC_Detector comprises three main components: the bug-fixing graph construction component, the code semantic aggregation component, and the cross-line semantic retention component. The bug-fixing graph construction component identifies the code syntax structures and program dependencies within bug-fixing commits and transforms them into heterogeneous graph formats by converting the source code into vector representations. The code semantic aggregation component adapts to heterogeneous data by using heterogeneous attention to learn the hidden semantic representation of target code lines. The cross-line semantic retention component regulates propagated semantic information by using attenuation and reinforcement gates derived from old and new code semantic representations, effectively preserving cross-line semantic relationships. Extensive experiments were conducted to evaluate the performance of our model by collecting data from 87 open-source projects, including 675 bug-fixing commits. The experimental results demonstrate that our model outperforms state-of-the-art approaches, achieving significant improvements of 83.15%,96.83%,78.71%,74.15%,54.14%,91.66%,91.66%, and 34.82% in MFR, respectively, compared with the state-of-the-art approaches.

cs.SE↗

arXiv2025

GP-GS: Gaussian Processes Densification for 3D Gaussian Splatting

3D Gaussian Splatting (3DGS) enables photorealistic rendering but suffers from artefacts due to sparse Structure-from-Motion (SfM) initialisation. To address this limitation, we propose GP-GS, a Gaussian Process (GP) based densification framework for 3DGS optimisation. GP-GS formulates point cloud densification as a continuous regression problem, where a GP learns a local mapping from 2D pixel coordinates to 3D position and colour attributes. An adaptive neighbourhood-based sampling strategy generates candidate pixels for inference, while GP-predicted uncertainty is used to filter unreliable predictions, reducing noise and preserving geometric structure. Extensive experiments on synthetic and real-world benchmarks demonstrate that GP-GS consistently improves reconstruction quality and rendering fidelity, achieving up to 1.12 dB PSNR improvement over strong baselines.

cs.CV↗

arXiv2023

Out-of-distribution Object Detection through Bayesian Uncertainty Estimation

The superior performance of object detectors is often established under the condition that the test samples are in the same distribution as the training data. However, in many practical applications, out-of-distribution (OOD) instances are inevitable and usually lead to uncertainty in the results. In this paper, we propose a novel, intuitive, and scalable probabilistic object detection method for OOD detection. Unlike other uncertainty-modeling methods that either require huge computational costs to infer the weight distributions or rely on model training through synthetic outlier data, our method is able to distinguish between in-distribution (ID) data and OOD data via weight parameter sampling from proposed Gaussian distributions based on pre-trained networks. We demonstrate that our Bayesian object detector can achieve satisfactory OOD identification performance by reducing the FPR95 score by up to 8.19% and increasing the AUROC score by up to 13.94% when trained on BDD100k and VOC datasets as the ID datasets and evaluated on COCO2017 dataset as the OOD dataset.

cs.CV↗

arXiv2022

Attacking Digital Twins of Robotic Systems to Compromise Security and Safety

Security and safety are of paramount importance to human-robot interaction, either for autonomous robots or human-robot collaborative manufacturing. The intertwined relationship between security and safety has imposed new challenges on the emerging digital twin systems of various types of robots. To be specific, the attack of either the cyber-physical system or the digital-twin system could cause severe consequences to the other. Particularly, the attack of a digital-twin system that is synchronized with a cyber-physical system could cause lateral damage to humans and other surrounding facilities. This paper demonstrates that for Robot Operating System (ROS) driven systems, attacks such as the person-in-the-middle attack of the digital-twin system could eventually lead to a collapse of the cyber-physical system, whether it is an industrial robot or an autonomous mobile robot, causing unexpected consequences. We also discuss potential solutions to alleviate such attacks.

cs.RO↗

arXiv2022

A Human-friendly Verbal Communication Platform for Multi-Robot Systems: Design and Principles

While multi-robot systems have been broadly researched and deployed, their success is built chiefly upon the dependency on network infrastructures, whether wired or wireless. Aiming at the first steps toward de-coupling the application of multi-robot systems from the reliance on network infrastructures, this paper proposes a human-friendly verbal communication platform for multi-robot systems, following the deliberately designed principles of being adaptable, transparent, and secure. The platform is network independent and is subsequently capable of functioning in network infrastructure lacking environments from underwater to planet explorations. A series of experiments were conducted to demonstrate the platform's capability in multi-robot systems communication and task coordination, showing its potential in infrastructure-free applications. To benefit the community, we have made the codes open source at https://github.com/jynxmagic/MSc_AI_project

cs.RO↗