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From the 1 of 7 linked papers with an AI index.

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7 papers

cs.CV2026

PanoAffordanceNet: Towards Holistic Affordance Grounding in 360° Indoor Environments

Guoliang Zhu, Wanjun Jia, Caoyang Shao +3

The paper defines holistic affordance grounding for 360° indoor scenes and presents PanoAffordanceNet, a model that corrects equirectangular distortion and densifies sparse predict…

cs.CV2026

Can we Trust Unreliable Voxels? Exploring 3D Semantic Occupancy Prediction under Label Noise

Wenxin Li, Kunyu Peng, Di Wen +6

3D semantic occupancy prediction is a cornerstone of robotic perception, yet real-world voxel annotations are inherently corrupted by structural artifacts and dynamic trailing effe…

cs.CV2026

What if? Emulative Simulation with World Models for Situated Reasoning

Ruiping Liu, Yufan Chen, Yuheng Zhang +8

Situated reasoning often relies on active exploration, yet in many real-world scenarios such exploration is infeasible due to physical constraints of robots or safety concerns of v…

cs.CV2026

O3N: Omnidirectional Open-Vocabulary Occupancy Prediction for Embodied Intelligent Robotics

Mengfei Duan, Hao Shi, Fei Teng +4

The rapid evolution of consumer electronics toward embodied intelligence has accelerated the emergence of Consumer Embodied Intelligent Robotics (CEIRs), where intelligent devices…

cs.CV2026

ProOOD: Prototype-Guided Out-of-Distribution 3D Occupancy Prediction

Yuheng Zhang, Mengfei Duan, Kunyu Peng +5

3D semantic occupancy prediction is central to autonomous driving, yet current methods are vulnerable to long-tailed class bias and out-of-distribution (OOD) inputs, often overconf…

cs.CV2026

Out-of-Distribution Semantic Occupancy Prediction

Yuheng Zhang, Mengfei Duan, Kunyu Peng +6

3D semantic occupancy prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distrib…