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YouTube-Occ learns 3D semantic occupancy from web videos

By PulseAugur Editorial · [1 sources] ·

Researchers have developed YouTube-Occ, a novel method for predicting 3D semantic occupancy from internet videos, addressing the scarcity of annotated 3D indoor data. The system utilizes a pipeline that processes raw web videos to estimate camera geometry, reconstruct point clouds, and generate semantic pseudo-labels using foundation models. To improve performance, a pre-training framework with dual-alignment strategies is introduced, including intra-frame alignment for 3D-to-2D feature matching and cross-scene alignment for semantic consistency. This approach has demonstrated consistent performance gains on the NYUv2 and Occ-ScanNet benchmarks, particularly in low-data scenarios. AI

IMPACT Enables more robust 3D scene understanding from readily available online video data.

RANK_REASON The item is an arXiv preprint detailing a new method for 3D semantic occupancy prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

YouTube-Occ learns 3D semantic occupancy from web videos

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The item is an arXiv preprint detailing a new method for 3D semantic occupancy prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Haoming Chen, Lichen Yuan, TianFang Sun, Jingyu Gong, Xin Tan, Zhizhong Zhang, Yanyun Qu, Yuan Xie ·

    YouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos

    arXiv:2506.18266v2 Announce Type: replace Abstract: 3D semantic occupancy prediction is crucial for fine-grained scene understanding, yet its advancement in privacy-sensitive indoor environments is fundamentally hindered by the scarcity of large-scale annotated 3D data. To overco…

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