LeanSplat: Low-Overhead Language Gaussian Splatting

Modeling 3D language fields with Gaussian Splatting suffers from high storage and computational costs due to the large number of Gaussian primitives, high-dimensional language features, and complicated retrieval procedures. Primitives from a pretrained 3DGS scene are typically carried into the language branch, so even those that contribute little to semantic prediction require high-dimensional language features and participate in semantic rendering. Query pipelines that reconstruct high-dimensional pixel-level language features before text matching repeat this high-dimensional computation for each query. In this paper, we propose LeanSplat, a low-overhead framework for Language Gaussian Splatting. LeanSplat alternates semantic-opacity optimization with multi-view language-feature aggregation to progressively refine primitive-level semantic visibility. The resulting cross-view semantic rendering contributions guide pruning of low-contribution primitives, while semantic opacity weights the construction of shared prototypes that compress the high-dimensional language features of retained primitives. At query time, text scores are computed directly in prototype space, mapped to primitives via prototype indices, and rasterized without reconstructing the full high-dimensional language feature field. Experiments on LERF show that LeanSplat removes 83.25% of language primitives while achieving an average mIoU of 65.5%. With a prototype budget of \(K=256\), the average language storage is reduced to 2.94 MiB/scene, while query throughput exceeds 500 queries/s on an A100 GPU.