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

• URL: http://arxiv.org/abs/2604.01388v1
• Date: Wed, 01 Apr 2026 20:48:06 GMT
• Title: LESV: Language Embedded Sparse Voxel Fusion for Open-Vocabulary 3D Scene Understanding
• Abstract summary: We present a novel framework that leverages Sparse Voxel Rasterization (SVRaster) as a structured, disjoint geometry representation.<n>This enables a deterministic, confidence-aware feature registration process and suppresses the semantic bleeding artifact common in 3DGS.<n>Our approach achieves state-of-the-art performance on Open Vocabulary 3D Object Retrieval and Point Cloud Understanding benchmarks.
• Score: 9.377694035678948
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 foundation model AM-RADIO, avoiding the computational overhead of hierarchical training methods. Our approach achieves state-of-the-art performance on Open Vocabulary 3D Object Retrieval and Point Cloud Understanding benchmarks, particularly excelling on fine-grained queries where registration methods typically fail.

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