mlsanityData to 14 Oct 2026

Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation

arXiv 2412.05969 ↗First posted 8 Dec 2024cs.CVcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 56 of its 73 references that are in the map. First posted on arXiv 8 Dec 2024.

Builds on

56 of 73 references.

References · 56

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38 more
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50 most cited of 56 All 56 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025TriDF: Triplane-Accelerated Density Fields for Few-Shot Remote Sensing Novel View Synthesiscited by 1
  2. 2026FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detectioncited by 0