Current 3D scene understanding methods are limited by offline-collected multi-view data or pre-constructed 3D geometry. In this paper, we present ExtractAnything3D (EA3D), a unified online framework for open-world 3D object extraction that enables simultaneous geometric reconstruction and holistic scene understanding. Given a streaming video, EA3D dynamically interprets each frame using vision-language and 2D vision foundation encoders to extract object-level knowledge. This knowledge is integrated and embedded into a Gaussian feature map via a feed-forward online update strategy. We then iteratively estimate visual odometry from historical frames and incrementally update online Gaussian features with new observations. A recurrent joint optimization module directs the model's attention to regions of interest, simultaneously enhancing both geometric reconstruction and semantic understanding. Extensive experiments across diverse benchmarks and tasks, including photo-realistic rendering, semantic and instance segmentation, 3D bounding box and semantic occupancy estimation, and 3D mesh generation, demonstrate the effectiveness of EA3D. Our method establishes a unified and efficient framework for joint online 3D reconstruction and holistic scene understanding, enabling a broad range of downstream tasks.
@article{arxiv.2510.25146,
title = {EA3D: Online Open-World 3D Object Extraction from Streaming Videos},
author = {Xiaoyu Zhou and Jingqi Wang and Yuang Jia and Yongtao Wang and Deqing Sun and Ming-Hsuan Yang},
journal= {arXiv preprint arXiv:2510.25146},
year = {2025}
}
Comments
The Thirty-Ninth Annual Conference on Neural Information Processing Systems(NeurIPS 2025)