← 研踪 PaperTrailCVF · CVPR 2026:cvf:fe357bfb8e7ce57a65178494fb7c808f · 记录 #1
Spatial Matters: Position-Guided 3D Referring Expression Segmentation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-01 23:19
摘要
3D Referring Expression segmentation (3D-RES) is an emerging field that segments 3D objects in point cloud scenes based on given referring expressions. Although existing methods have achieved substantial progress, they primarily focus on semantic cues and often overlook spatial relations, which are essential for segmenting the referred objects in complex 3D scenes, especially those containing multiple visually similar instances. In this paper, we propose Position3D, a novel approach that explicitly incorporates spatial relation modeling into 3D-RES. Specifically, we introduce a spatial-aware query generation module that constructs point proxies by aggregating local context and incorporating spatial relations, from which the most text-relevant are selected as queries. Furthermore, we design a position-guided deformable attention in the decoder, which progressively refines attention to concentrate on the target object under positional relationship guidance. Extensive experiments on two benchmark datasets, i.e., ScanRefer, and Multi3DRefer, validate the effectiveness of the proposed method Position3D.
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记录 #1 · 2026 · 最新观测版本
Spatial Matters: Position-Guided 3D Referring Expression Segmentation
3D Referring Expression segmentation (3D-RES) is an emerging field that segments 3D objects in point cloud scenes based on given referring expressions. Although existing methods have achieved substantial progress, they primarily focus on semantic cues and often overlook spatial relations, which are essential for segmenting the referred objects in complex 3D scenes, especially those containing multiple visually similar instances. In this paper, we propose Position3D, a novel approach that explicitly incorporates spatial relation modeling into 3D-RES. Specifically, we introduce a spatial-aware query generation module that constructs point proxies by aggregating local context and incorporating spatial relations, from which the most text-relevant are selected as queries. Furthermore, we design a position-guided deformable attention in the decoder, which progressively refines attention to concentrate on the target object under positional relationship guidance. Extensive experiments on two benchmark datasets, i.e., ScanRefer, and Multi3DRefer, validate the effectiveness of the proposed method Position3D.
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