# Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds

Source: [Synthetic-to-Real Domain Generalized Semantic Segmentation for 3D Indoor Point Clouds](<https://www.sota2.com/research/paper/68fa6e2d1e209131cea3e5a8>)

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

Data updated: 2025-10-26T00:51:11.369Z

Authors: Yuyang Zhao, Na Zhao, Gim Hee Lee

Published: 2022-12-09T05:07:43.000Z

## Abstract

Semantic segmentation in 3D indoor scenes has achieved remarkable performance under the supervision of large-scale annotated data. However, previous works rely on the assumption that the training and testing data are of the same distribution, which may suffer from performance degradation when evaluated on the out-of-distribution scenes. To alleviate the annotation cost and the performance degradation, this paper introduces the synthetic-to-real domain generalization setting to this task. Specifically, the domain gap between synthetic and real-world point cloud data mainly lies in the different layouts and point patterns. To address these problems, we first propose a clustering instance mix (CINMix) augmentation technique to diversify the layouts of the source data. In addition, we augment the point patterns of the source data and introduce non-parametric multi-prototypes to ameliorate the intra-class variance enlarged by the augmented point patterns. The multi-prototypes can model the intra-class variance and rectify the global classifier in both training and inference stages. Experiments on the synthetic-to-real benchmark demonstrate that both CINMix and multi-prototypes can narrow the distribution gap and thus improve the generalization ability on real-world datasets.

## 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/2212.04668>)

[PDF](<https://arxiv.org/pdf/2212.04668.pdf>)