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Haonan Luo

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3 papers

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AAAI Conference 2026 Conference Paper

SparseSurf: Sparse-View 3D Gaussian Splatting for Surface Reconstruction

Meiying Gu
Jiawei Zhang
Jiahe Li
Xiaohan Yu
Haonan Luo
Jin Zheng
Xiao Bai

Recent advances in optimizing Gaussian Splatting for scene geometry have enabled efficient reconstruction of detailed surfaces from images. However, when input views are sparse, such optimization is prone to overfitting, leading to suboptimal reconstruction quality. Existing approaches address this challenge by employing flattened Gaussian primitives to better fit surface geometry, combined with depth regularization to alleviate geometric ambiguities under limited viewpoints. Nevertheless, the increased anisotropy inherent in flattened Gaussians exacerbates overfitting in sparse-view scenarios, hindering accurate surface fitting and degrading novel view synthesis performance. In this paper, we propose SparseSurf, a method that reconstructs more accurate and detailed surfaces while preserving high-quality novel view rendering. Our key insight is to introduce Stereo Geometry-Texture Alignment, which bridges rendering quality and geometry estimation, thereby jointly enhancing both surface reconstruction and view synthesis. In addition, we present a Pseudo-Feature Enhanced Geometry Consistency that enforces multi-view geometric consistency by incorporating both training and unseen views, effectively mitigating overfitting caused by sparse supervision. Extensive experiments on the DTU, BlendedMVS, and Mip-NeRF360 datasets demonstrate that our method achieves the state-of-the-art performance.

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AAAI Conference 2025 Conference Paper

Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration

Zhixuan Shen
Haonan Luo
Kexun Chen
Fengmao Lv
Tianrui Li

Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach.

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EAAI Journal 2025 Journal Article

LIVFusion: Luminance-optimized fusion of infrared and visible images with wavelet transformer

Dingli Hua
Qingmao Chen
Wenying Wen
Haonan Luo

In the field of image processing, effective fusion of infrared and visible images remains a significant challenge, particularly in terms of illumination adjustment and detail preservation. Current methods often struggle to adequately balance these aspects, resulting in degraded visual perception and texture detail. This compromises the ability to accurately infer realistic details and restore true colors, which are crucial for subsequent applications. To address these limitations, this study introduces an innovative image fusion framework called luminance-optimized fusion of infrared and visible images with wavelet transformer (LIVFusion). Specifically, considering that the wavelet transform can effectively capture local and global features of images, we propose a wavelet transformer fusion network (WTFNet) to enhance the texture details of the fused features. Furthermore, to further improve the fusion quality of images under low-light conditions, we integrate the scene-illumination disentangled network (SIDNet) with the WTFNet. This combination achieves precise brightness adjustment and superior feature integration, significantly improving the quality of image processing. Additionally, we propose a contrast equilibration loss to ensure harmonious integration of details and clarity across different lighting scenarios. Extensive evaluations demonstrate that our framework outperforms existing state-of-the-art technologies in rendering natural appearances and fine textures, thereby enhancing night image analysis for critical visual applications.

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