← 研踪 PaperTrailCVF · CVPR 2026:cvf:ab3ccab2fcb96327fc1fb41900837ee8 · 记录 #1
GenSplat: Bridging the Generalization Gap in 3DGS Language Comprehension
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 02:33
摘要
In this paper, we propose GenSplat, a novel approach for language comprehension in 3D Gaussian Splatting (3DGS). Unlike previous methods that either achieve cross-scene generalization by being bounded to a predefined vocabulary or handle free-form language by overfitting to individual scenes, GenSplat is robust to free-form language queries and generalizable across 3DGS scene representations. Our key insight for this problem is to formulate a structured learning process to progressively align linguistic concepts with 3D Gaussians. It contains two novel technical contributions. First, we propose a Progressive Language Grounding Curriculum that structurally guides the model through learning category-level semantics to instance-level concepts and free-form language, preventing overfitting by building a generalizable language feature space. Second, we design a Multi-modal Large Language Models (MLLM)-guided Reasoning Module that leverages MLLM's semantic and spatial priors to enhance 3D localization and reasoning. To further improve spatial alignment and computational efficiency, we introduce a GeometryAware Frame Selector (GAFS), which adaptively selects the most informative views based on Gaussian and textural cues. Extensive cross-task evaluations (including 3D referring segmentation, 3D visual question answering, and 3D open-vocabulary understanding) demonstrate state-of-the-art performances and strong generalization capability of GenSplat. We will release the codes.
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记录 #1 · 2026 · 最新观测版本
GenSplat: Bridging the Generalization Gap in 3DGS Language Comprehension
In this paper, we propose GenSplat, a novel approach for language comprehension in 3D Gaussian Splatting (3DGS). Unlike previous methods that either achieve cross-scene generalization by being bounded to a predefined vocabulary or handle free-form language by overfitting to individual scenes, GenSplat is robust to free-form language queries and generalizable across 3DGS scene representations. Our key insight for this problem is to formulate a structured learning process to progressively align linguistic concepts with 3D Gaussians. It contains two novel technical contributions. First, we propose a Progressive Language Grounding Curriculum that structurally guides the model through learning category-level semantics to instance-level concepts and free-form language, preventing overfitting by building a generalizable language feature space. Second, we design a Multi-modal Large Language Models (MLLM)-guided Reasoning Module that leverages MLLM's semantic and spatial priors to enhance 3D localization and reasoning. To further improve spatial alignment and computational efficiency, we introduce a GeometryAware Frame Selector (GAFS), which adaptively selects the most informative views based on Gaussian and textural cues. Extensive cross-task evaluations (including 3D referring segmentation, 3D visual question answering, and 3D open-vocabulary understanding) demonstrate state-of-the-art performances and strong generalization capability of GenSplat. We will release the codes.
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