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Guichen Huang

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AAAI Conference 2026 Conference Paper

LongSplat: Online Generalizable 3D Gaussian Splatting from Long Sequence Images

Guichen Huang
Ruoyu Wang
Xiangjun Gao
Che Sun
Yuwei Wu
Shenghua Gao
Yunde Jia

3D Gaussian Splatting (3DGS) achieves high-fidelity novel view synthesis, but its application in online long-sequence scenarios is still restricted. Existing methods either rely on slow per-scene optimization or lack efficient frame-wise 3DGS updates, making them unsuitable for online long-sequence videos. In this paper, we propose LongSplat, an online real-time 3D Gaussian reconstruction framework designed for long-sequence image input. The core idea of LongSplat is to maintain a global 3DGS set and design a streaming 3DGS update mechanism that selectively compressing redundant historical Gaussians and introducing new Gaussians by comparing the current observations with the historical Gaussian. To achieve this goal, we design a Gaussian-Image Representation (GIR), which encodes 3D Gaussian parameters into a structured, image-like 2D format. GIR simultaneously enables identity-aware redundancy compression as well as the fusion of current view and historical Gaussians, which are used for online reconstruction and adapt the model to long sequences without overwhelming memory or computational costs. Extensive experiments demonstrate that LongSplat achieves state-of-the-art efficiency-quality trade-offs in real-time novel view synthesis, delivering real-time reconstruction while reducing Gaussian counts by 44% compared to per-pixel prediction paradigms.

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IROS Conference 2025 Conference Paper

OVSG-SLAM: Open-Vocabulary Semantic Gaussian Splatting SLAM

Zhehang Liu
Shishen Li
Guichen Huang
Yuwei Wu

Most conventional semantic SLAM approaches concentrate on maintaining 3D semantic consistency while overlooking their reliance on predefined semantic categories, ultimately limiting flexibility in scene understanding. We propose Open-Vocabulary Semantic Gaussian Splatting SLAM (OVSG-SLAM), an approach that integrates multi-modal perception and 3D Gaussian splatting into a semantic SLAM framework. By combining the advantages of Segment Anything (SAM) for open-vocabulary 2D scene understanding with the powerful feature extraction capabilities of vision-language models, our method eliminates the reliance on predefined closed-set categories. Although Vision-Language Models (VLMs) provide open-vocabulary reasoning, integrating them with 3D semantic SLAM poses challenges such as embedding ambiguity and computational overhead. To address these challenges, we present a feature embedding strategy called differentiable identity-aware encoding, which reduces computational cost while ensuring accurate semantic mapping. Furthermore, instead of using a traditional semantic loss, we optimize the scene representation through an identity loss. Extensive experimental evaluations on the Replica and ScanNet datasets demonstrate that the proposed method achieves state-of-the-art performance in mapping, tracking and 3D semantic segmentation tasks.

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