跳到主要內容
AI News HubLIVE
來源內容 · 翻譯待補全2 分鐘閱讀

待翻譯:DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians

文章摘要

AI 服務暫時不可用,以下為來源摘要,待恢復後補全翻譯:arXiv:2610.00040v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation models into 3D representations. However, existing referring fields embed language features in a globally view-invariant space, making them fundamentally unable to resolve observer-centric spatial relations (e.g., "to the left of") that depend on camera pose. We propose DSSR-3D, an inference-time framework for view-dependent referring segmentation on continuous 3D Gaussian fields, formalized as two interfaces - pose-invariant semantic localization and pose-conditioned spatial reasoning - such that any pair of functions satisfying these constraints yields a valid instan…

來源arXiv Computer Vision作者: Thanh-Khoi Nguyen, Thien-Phuc Tran, Minh-Triet Tran
待翻譯:DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians
回報錯誤

更正管道尚未開通,可先複製下方文章資訊留存。

查看更正說明
直接讀正文

AI 服務暫時不可用,以下為來源正文,待恢復後補全翻譯。

[Submitted on 3 Sep 2026] Title:DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians View a PDF of the paper titled DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians, by Thanh-Khoi Nguyen and 2 other authors View PDF HTML (experimental) Abstract:Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge from 2D foundation models into 3D representations. However, existing referring fields embed language features in a globally view-invariant space, making them fundamentally unable to resolve observer-centric spatial relations (e.g., "to the left of") that depend on camera pose. We propose DSSR-3D, an inference-time framework for view-dependent referring segmentation on continuous 3D Gaussian fields, formalized as two interfaces - pose-invariant semantic localization and pose-conditioned spatial reasoning - such that any pair of functions satisfying these constraints yields a valid instantiation, requiring no retraining of the underlying semantic field and no reliance on discrete geometric proxies such as bounding boxes. We instantiate the two interfaces with a temperature-sharpened softmax localization mechanism and a projection-based directional scoring function, fused via a lightweight, training-free step, and show they transfer zero-shot to structurally distinct semantic fields without adaptation. We further propose ViewRef-GS, a benchmark isolating view-dependent segmentation on 3D Gaussian fields, evaluated jointly with an augmented Ref-LERF to provide a comprehensive testbed for viewpoint-dependent spatial grounding. Experiments show consistent gains over existing 3DGS-based referring methods, with no additional training beyond the base semantic field Subjects: Computer Vision and Pattern Recognition (cs.CV) Cite as: arXiv:2610.00040 [cs.CV] (or arXiv:2610.00040v1 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2610.00040 arXiv-issued DOI via DataCite Submission history From: Khoi Nguyen [view email] [v1] Thu, 3 Sep 2026 01:27:45 UTC (2,785 KB) Full-text links: Access Paper: View a PDF of the paper titled DSSR-3D: Decoupled Reasoning for View-Dependent Referring in 3D Gaussians, by Thanh-Khoi Nguyen and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV new | recent | 2026-10 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

展開要點與分析

文章情報

工程師進階

要點

  • AI 服務暫時不可用,系統已先保留來源內容與降級後設資料。
  • arXiv:2610.00040v1 Announce Type: new Abstract: Recent advances in 3D Gaussian Splatting have enabled open-vocabulary and referring segmentation by distilling semantic knowledge f…

要點與分析由自動化流程生成,可能有誤,請結合原始來源核實。