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Xiaohui Chu

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3 papers

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AAAI Conference 2026 Conference Paper

DSP-PCQA: Integrating Multiple Perception Preferences for Point Cloud Quality Assessment

Mingxuan Li
Fazhan Zhang
Zhenzhe Hou
Zihao Huang
Bohan Fu
Runze Hu
Xiaohui Chu

Point Cloud Quality Assessment (PCQA) faces a critical disconnect: existing methods operate on a flawed single-perception paradigm, while human observers evaluate quality through dual cognitive streams: technical rationality and semantic sensibility. This fundamental mismatch routinely produces assessment failures in real-world scenarios where technical and semantic signals conflict. To address this, we introduce Dual-Stream Perception PCQA (DSP-PCQA), the first framework that explicitly models this perceptual duality through parallel networks thoroughly mirroring the human cognitive pathway. DSP-PCQA introduces three key innovations: (1) a Decoupled Focus Enhancer (DFE) that surgically isolates technical and semantic information using two targeted transformations; (2) a Context & Attribute Correlation Awareness (CACA) module that captures the dynamic, non-linear relationships between different views and sub-models characteristic of human visual processing; and (3) an Exchange-based Perceptual Injection (EPI) module that strategically transfers information between perception streams, simulating how humans integrate multiple perceptual dimensions. Extensive evaluations show DSP-PCQA outperforms state-of-the-art methods across multiple benchmarks. Most importantly, our method resolves the perceptual discord that plagues existing approaches, maintaining high accuracy even in the challenging boundary cases where technical quality and semantic significance diverge, precisely where conventional methods often struggle.

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AAAI Conference 2026 Conference Paper

FVNet: Harnessing Liquid Neural Dynamics for Lightweight Visual Representation

Zhenzhe Hou
Xiaohui Chu
Runze Hu
Yang Li
Yutao Liu

Efficient visual backbone design remains crucial for resource-constrained computer vision applications. Inspired by the adaptive continuous-time dynamics observed in biological neurons, we propose FVNet, a novel lightweight architecture that integrates liquid neural dynamics for efficient and dynamic visual feature extraction. Central to FVNet is the Fluid Temporal Flow Unit (FTFU), which employs continuous-time equations with learnable time constants to capture spatio-temporal dependencies adaptively. By further stacking these units in a Multi-Phase Fluid Block (MPFB), our model processes features across parallel temporal scales, enabling context-aware feature encoding without incurring excessive computational overhead. Through a discrete closed-form solution, FVNet achieves the representational power of continuous-time models while avoiding the instability and overhead of iterative numerical solvers. Extensive experiments on various vision tasks demonstrate that FVNet achieves superior performance and efficiency over existing state-of-the-art lightweight networks.

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AAAI Conference 2026 Conference Paper

Points Meet Pixels: Bridging 2D Vision-Language Model and 3D Perception Gaps for Point Cloud Quality Assessment

Mingxuan Li
Zihao Huang
Xiaohui Chu
Fazhan Zhang
Bohan Fu
Runze Hu

Vision-Language Models (VLMs) have demonstrated significant progress in quality assessment tasks. However, a fundamental paradox arises when their application to Point Cloud Quality Assessment (PCQA). Existing VLMs, designed for image-text pairs, are inherently incompatible with 3D point cloud data due to the modality gap. While some PCQA research attempts to adapt point clouds to VLMs by 2D projection, this approach inevitably sacrifices crucial spatial structure information essential for accurate quality assessment. Conversely, directly integrating a dedicated 3D branch into a VLM-based PCQA framework introduces feature space misalignment and an influx of quality-insensitive information. To bridge these fundamental conflicts hindering VLMs' adaptation to PCQA, we propose the PMP-PCQA framework, which leverages the inherent mapping relationship between points and pixels to seamlessly apply VLMs to PCQA. Our approach introduces three key innovations: a Spatial Awareness Enhancer(SAE) module that enriches the image features with spatial coordinate clues to reinforce geometric awareness in 2D visual representations; a Fine-to-coarse Consistency Alignment(FCA) module that bridges the gap between 2D and 3D modalities by leveraging point-pixel correspondences to construct bridging features; and a Text-Guided Adaptive Miner(TAM) module that dynamically suppresses quality-insensitive features to mine discriminative visual clues for PCQA. Extensive evaluations demonstrate that PMP-PCQA consistently outperforms state-of-the-art methods across multiple benchmarks.

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