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Qi Wu

7 papers indexed

arxivcs.GRcs.AIcs.CV2026-07-15

Instant NuRec: Feed-Forward 3D Gaussian Reconstruction for Driving Scene Simulation

NVIDIA, :, Jiahui Huang, Jiawei Ren, Michal Tyszkiewicz, Bjoern Haefner, et al.

3D simulation platforms are critical for autonomous driving because they enable end-to-end policy evaluation, thereby reducing development costs and improving safety. In recent years, neural simulation has become predominant, with methods such as NuRec playing a central role; how…

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arxiveess.SP2026-07-06

A Body-of-Revolution Human Model for RF Sensing with Measurement-Driven Calibration for Indoor Environments

Haoqing Wen, Michele D'Amico, Matteo Oldoni, Federica Fieramosca, Vittorio Rampa, Stefano Savazzi, et al.

Model training for Device-Free Localization (DFL) and Radio-Frequency (RF) sensing systems heavily relies on large-scale datasets, which are costly and time-consuming to obtain through measurements across different environments and sensing configurations. Lightweight yet physical…

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arxivcs.ROcs.CV2026-07-04

From Region Arrival to Instance-Level Grounding in Vision-and-Language Navigation

Xiangyu Shi, Ruoxi Yang, Wei Tao, Jiwen Zhang, Yanyuan Qiao, Qi Wu

Vision-and-Language Navigation (VLN) agents may satisfy conventional success criteria while still failing to establish reliable object-level grounding, because current evaluation protocols mainly reward stopping within a 3-meter radius and largely ignore the agent's final orienta…

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arxivcs.RO2026-07-04

HyperDCM: Dynamic Cluster Memory Replay in Hyperbolic Space for Continual Robotic Navigation Across Scenes

Zhengfei Lu, Jian Yang, Muyu Wang, Shaowen Chen, Jinpeng Mi, Ke Li, et al.

Continual learning in visual navigation remains challenging due to catastrophic forgetting and the difficulties associated with adapting to diverse and evolving environments. To address these issues, we propose Hyperbolic Dynamic Cluster Memory (HyperDCM), a structure-aware memor…

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arxivcs.LG2026-07-01

Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation

Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben, Steve Petruzza, Qi Wu, Will Usher, et al.

Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint. However, as existing INRs for unstructured volumes do not encode ge…

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arxivcs.CVcs.AI2026-06-26

Home3D 1.0: A High-Fidelity Image-to-3D Asset Generation System for Interior Design

Yiyun Fei, Guoqiu Li, Jin Song, Chuqiao Wu, Delong Wu, Hong Wu, et al.

We present Home3D 1.0, a modular image-to-3D generation system that produces high-quality 3D assets from a single reference image, targeting interior design and e-commerce applications. Given a photograph of a furniture or decor item, the system outputs a mesh with physically-bas…

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