# Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation

Source: [Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation](<https://www.sota2.com/research/paper/68fa76231e209131cea6b700>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:43:58.474Z

Authors: Zipeng Qi, Hao Chen, Haotian Zhang, Zhengxia Zou, Zhenwei Shi

Published: 2024-12-08T15:28:30.000Z

## Abstract

In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic features of point clouds onto the image plane, simultaneously rendering RGB images and semantic segmentation results. Leveraging the explicit structure of point clouds and a one-time rendering strategy, our approach significantly enhances efficiency during optimization and rendering. Additionally, we employ SAM2 to generate pseudo-labels for boundary regions, which often lack sufficient supervision, and introduce two-level aggregation losses at the 2D feature map and 3D spatial levels to improve the view-consistent and spatial continuity.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2412.05969>)

[PDF](<https://arxiv.org/pdf/2412.05969.pdf>)