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From the 1 of 8 linked papers with an AI index.

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20242026
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cs.CV2025

OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding

Dianyi Yang, Xihan Wang, Yu Gao +4

Recent advancements in 3D scene understanding have made significant strides in enabling interaction with scenes using open-vocabulary queries, particularly for VR/AR and robotic ap…

cs.CV2025

Automated 3D-GS Registration and Fusion via Skeleton Alignment and Gaussian-Adaptive Features

Shiyang Liu, Dianyi Yang, Yu Gao +3

In recent years, 3D Gaussian Splatting (3D-GS)-based scene representation demonstrates significant potential in real-time rendering and training efficiency. However, most existing…

cs.CV2025

DroneSplat: 3D Gaussian Splatting for Robust 3D Reconstruction from In-the-Wild Drone Imagery

Jiadong Tang, Yu Gao, Dianyi Yang +3

Drones have become essential tools for reconstructing wild scenes due to their outstanding maneuverability. Recent advances in radiance field methods have achieved remarkable rende…

cs.CV2025

GaussianGraph: 3D Gaussian-based Scene Graph Generation for Open-world Scene Understanding

Xihan Wang, Dianyi Yang, Yu Gao +3

Recent advancements in 3D Gaussian Splatting(3DGS) have significantly improved semantic scene understanding, enabling natural language queries to localize objects within a scene. H…

cs.CV2025

OpenGS-SLAM: Open-Set Dense Semantic SLAM with 3D Gaussian Splatting for Object-Level Scene Understanding

Dianyi Yang, Yu Gao, Xihan Wang +3

Recent advancements in 3D Gaussian Splatting have significantly improved the efficiency and quality of dense semantic SLAM. However, previous methods are generally constrained by l…

cs.CV2024

MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results

Jiangpeng He, Yuhao Chen, Gautham Vinod +16

The increasing interest in computer vision applications for nutrition and dietary monitoring has led to the development of advanced 3D reconstruction techniques for food items. How…