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

Source: [SegSplat: Feed-forward Gaussian Splatting and Open-Set Semantic Segmentation](<https://www.sota2.com/research/paper/6925380cd6b0dd334292f3f5>)

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

Data updated: 2025-11-26T05:03:02.866Z

Authors: Peter Siegel, Federico Tombari, Marc Pollefeys, Daniel Barath

Published: 2025-11-23T10:26:38.000Z

## 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.

## 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/2511.18386>)

[PDF](<https://arxiv.org/pdf/2511.18386.pdf>)