SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation

• URL: http://arxiv.org/abs/2511.18386v1
• Date: Sun, 23 Nov 2025 10:26:38 GMT
• Title: SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation
• Authors: Peter Siegel, Federico Tombari, Marc Pollefeys, Daniel Barath,
• Abstract summary: SegSplat is a novel framework designed to bridge the gap between rapid, feed-forward 3D reconstruction and rich, open-vocabulary semantic understanding.<n>This work represents a significant step towards practical, on-the-fly generation of semantically aware 3D environments.
• Score: 114.57192386025373
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We have introduced SegSplat, a novel framework designed to bridge the gap between rapid, feed-forward 3D reconstruction and rich, open-vocabulary semantic understanding. By constructing a compact semantic memory bank from multi-view 2D foundation model features and predicting discrete semantic indices alongside geometric and appearance attributes for each 3D Gaussian in a single pass, SegSplat efficiently imbues scenes with queryable semantics. Our experiments demonstrate that SegSplat achieves geometric fidelity comparable to state-of-the-art feed-forward 3D Gaussian Splatting methods while simultaneously enabling robust open-set semantic segmentation, crucially \textit{without} requiring any per-scene optimization for semantic feature integration. This work represents a significant step towards practical, on-the-fly generation of semantically aware 3D environments, vital for advancing robotic interaction, augmented reality, and other intelligent systems.

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