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most citedMulti-Space Alignments Towards Universal LiDAR Segmentation

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cs.CV2026

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

Xiang Xu, Alan Liang, Youquan Liu +5

Constructing faithful 4D worlds from LiDAR-acquired sequences is crucial for embodied AI, yet current generative frameworks apply uniform modeling capacity across all spatial regio…

cs.CV2026

SpatialBench: Is Your Spatial Foundation Model an All-Round Player?

Haosong Peng, Hao Li, Jiaqi Chen +10

While spatial foundation models have demonstrated impressive performance on standard datasets, a critical question remains: are they truly all-round players capable of generalizing…

cs.CV2026

From Pixels to Words -- Towards Native One-Vision Models at Scale

Haiwen Diao, Jiahao Wang, Penghao Wu +18

Current vision-language models (VLMs) typically stitch together separate image encoders and language decoders via multi-stage alignment, a modular framework that inevitably fragmen…

cs.CV2026

LLaVA-OneVision-2: Towards Next-Generation Perceptual Intelligence

Xiang An, Yin Xie, Feilong Tang +27

We introduce LLaVA-OneVision-2 (LLaVA-OV-2), the most capable vision-language model in the LLaVA-OneVision series to date, achieving superior performance across a broad range of mu…

cs.CV2026

PhysX-Omni: Unified Simulation-Ready Physical 3D Generation for Rigid, Deformable, and Articulated Objects

Ziang Cao, Yinghao Liu, Haitian Li +5

Simulation-ready physical 3D assets have emerged as a promising direction owing to their broad applicability in downstream tasks. However, most existing 3D generation methods eithe…

cs.CV2026

OnlineSI: Taming Large Language Model for Online 3D Understanding and Grounding

Zixian Liu, Zhaoxi Chen, Liang Pan +1

In recent years, researchers have increasingly been interested in how to enable Multimodal Large Language Models (MLLM) to possess spatial understanding and reasoning capabilities.…