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20242026
most citedLearning 2D Invariant Affordance Knowledge for 3D Affordance Grounding

6 citations · 6 across the 9 of their papers we have counts for

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cs.CV2026

Efficient Diffusion as Low Light Enhancer

Guanzhou Lan, Qianli Ma, Yuqi Yang +4

The computational burden of the iterative sampling process remains a major challenge in diffusion-based Low-Light Image Enhancement (LLIE). Current acceleration methods, whether tr…

cs.CV2026

Open-Vocabulary Octree-Graph for 3D Scene Understanding

Zhigang Wang, Yifei Su, Chenhui Li +4

Open-vocabulary 3D scene understanding is indispensable for embodied agents. Recent works leverage pretrained vision-language models (VLMs) for object segmentation and project them…

cs.CV2026

FreeGaussian: Annotation-free Control of Articulated Objects via 3D Gaussian Splats with Flow Derivatives

Qizhi Chen, Delin Qu, Junli Liu +5

Reconstructing controllable Gaussian splats for articulated objects from monocular video is especially challenging due to its inherently insufficient constraints. Existing methods…

cs.CV20266 cited

Learning 2D Invariant Affordance Knowledge for 3D Affordance Grounding

Xianqiang Gao, Pingrui Zhang, Delin Qu +4

3D Object Affordance Grounding aims to predict the functional regions on a 3D object and has laid the foundation for a wide range of applications in robotics. Recent advances tackl…

cs.CV2026

Understanding Degradation with Vision Language Model

Guanzhou Lan, Chenyi Liao, Yuqi Yang +5

Understanding visual degradations is a critical yet challenging problem in computer vision. While recent Vision-Language Models (VLMs) excel at qualitative description, they often…

cs.CV2025

Exploring the Potential of Encoder-free Architectures in 3D LMMs

Yiwen Tang, Zoey Guo, Zhuhao Wang +8

Encoder-free architectures have been preliminarily explored in the 2D Large Multimodal Models (LMMs), yet it remains an open question whether they can be effectively applied to 3D…