Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2512.20927v1
• Date: Wed, 24 Dec 2025 04:16:18 GMT
• Title: Quantile Rendering: Efficiently Embedding High-dimensional Feature on 3D Gaussian Splatting
• Authors: Yoonwoo Jeong, Cheng Sun, Frank Wang, Minsu Cho, Jaesung Choe,
• Abstract summary: Recent advancements in computer vision have successfully extended Open-vocabulary segmentation (OVS) to the 3D domain by leveraging 3D Gaussian Splatting (3D-GS)<n>Existing methods employ codebooks or feature compression, causing information loss, thereby degrading segmentation quality.<n>We introduce Quantile Rendering (Q-Render), a novel rendering strategy for 3D Gaussians that efficiently handles high-dimensional features while maintaining high fidelity.<n>Our framework outperforms state-of-the-art methods, while enabling real-time rendering with an approximate 43.7x speedup on 512-D feature maps.
• Score: 52.18697134979677
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advancements in computer vision have successfully extended Open-vocabulary segmentation (OVS) to the 3D domain by leveraging 3D Gaussian Splatting (3D-GS). Despite this progress, efficiently rendering the high-dimensional features required for open-vocabulary queries poses a significant challenge. Existing methods employ codebooks or feature compression, causing information loss, thereby degrading segmentation quality. To address this limitation, we introduce Quantile Rendering (Q-Render), a novel rendering strategy for 3D Gaussians that efficiently handles high-dimensional features while maintaining high fidelity. Unlike conventional volume rendering, which densely samples all 3D Gaussians intersecting each ray, Q-Render sparsely samples only those with dominant influence along the ray. By integrating Q-Render into a generalizable 3D neural network, we also propose Gaussian Splatting Network (GS-Net), which predicts Gaussian features in a generalizable manner. Extensive experiments on ScanNet and LeRF demonstrate that our framework outperforms state-of-the-art methods, while enabling real-time rendering with an approximate ~43.7x speedup on 512-D feature maps. Code will be made publicly available.

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