[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
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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)
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