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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.CV2026

ABot-3DWorld 0: A Universal World Model to Explore Any 3D Space

Mingchao Sun, Luyang Tang, Yu Liu +34

The paper introduces ABot-3DWorld 0, a multimodal system that converts text, images, or video into high‑fidelity, explorable 3D worlds using a compact spatial representation and pa…

cs.GR2026

LagrangianSplats: Divergence-Free Transport of Gaussian Primitives for Fluid Reconstruction

Ningxiao Tao, Baoquan Chen, Mengyu Chu

Reconstructing 3D fluid velocity fields from sparse 2D video observations is a highly ill-posed inverse problem, demanding both transport consistency with observed motion and physi…

cs.GR2026

FieryGS: In-the-Wild Fire Synthesis with Physics-Integrated Gaussian Splatting

Qianfan Shen, Ningxiao Tao, Qiyu Dai +6

We consider the problem of synthesizing photorealistic, physically plausible combustion effects in in-the-wild 3D scenes. Traditional CFD and graphics pipelines can produce realist…

cs.CV2026

The Less You Depend, The More You Learn: Synthesizing Novel Views from Sparse, Unposed Images with Minimal 3D Knowledge

Haoru Wang, Kai Ye, Minghan Qin +3

Recent advances in feed-forward Novel View Synthesis (NVS) have led to a divergence between two design philosophies: bias-driven methods, which rely on explicit 3D knowledge, such…

cs.CV2026

From Orbit to Ground: Generative City Photogrammetry from Extreme Off-Nadir Satellite Images

Fei Yu, Yu Liu, Luyang Tang +10

City-scale 3D reconstruction from satellite imagery presents the challenge of extreme viewpoint extrapolation, where our goal is to synthesize ground-level novel views from sparse…

cs.CV2026

PointCNN++: Performant Convolution on Native Points

Lihan Li, Haofeng Zhong, Rui Bu +4

Existing convolutional learning methods for 3D point cloud data are divided into two paradigms: point-based methods that preserve geometric precision but often face performance cha…