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arXiv · 2604.01388

LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding

Fusang Wang·Nathan Piasco·Moussab Bennehar·Luis Roldão·Dzmitry Tsishkou·Fabien Moutarde
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Abstract

Recent advancements in open-vocabulary 3D scene understanding heavily rely on 3D Gaussian Splatting (3DGS) to register vision-language features into 3D space. However, we identify two critical limitations in these approaches: the spatial ambiguity arising from unstructured, overlapping Gaussians which necessitates probabilistic feature registration, and the multi-level semantic ambiguity caused by pooling features over object-level masks, which dilutes fine-grained details. To address these challenges, we present a novel framework that leverages Sparse Voxel Rasterization (SVRaster) as a structured, disjoint geometry representation. By regularizing SVRaster with monocular depth and normal priors, we establish a stable geometric foundation. This enables a deterministic, confidence-aware feature registration process and suppresses the semantic bleeding artifact common in 3DGS. Furthermore, we resolve multi-level ambiguity by exploiting the emerging dense alignment properties of the AM-RADIO foundation model, avoiding the computational overhead of hierarchical training methods. Our approach achieves state-of-the-art performance on Open Vocabulary Point Cloud Understanding, and highly competitive results on 3D and 2D Object Retrieval benchmarks.

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Fusang Wang, Nathan Piasco, Moussab Bennehar, Luis Roldão, Dzmitry Tsishkou, Fabien Moutarde. 2026-04-01. LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding. https://arxiv.org/abs/2604.01388

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