SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation

SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation

European Conference on Machine Learning (ECML-PKDD)

10.1007/978-3-032-05962-8_4

https://doi.org/10.1007/978-3-032-05962-8_4

Adam Goodge, Xun Xu, Bryan Hooi, Wee Siong Ng, Jingyi Liao, Yongyi Su, Yang Xulei

out of distribution, point clouds, Vision language model

04 October 2025

Goodge, A. et al. (2026). SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation. In: Ribeiro, R.P., et al. Machine Learning and Knowledge Discovery in Databases. Research Track. ECML PKDD 2025. Lecture Notes in Computer Science(), vol 16013. Springer, Cham. https://doi.org/10.1007/978-3-032-05962-8_4

As point cloud data increases in prevalence in a variety of applications, the ability to detect out-of-distribution (OOD) point cloud objects becomes critical for ensuring model safety and reliability. However, this problem remains under-explored in existing research. Inspired by success in the image domain, we propose to exploit advances in 3D vision-language models (3D VLMs) for OOD detection in point cloud objects. However, a major challenge is that point cloud datasets used to pre-train 3D VLMs are drastically smaller in size and object diversity than their image-based counterparts. Critically, they often contain exclusively computer-designed synthetic objects. This leads to a substantial domain shift when the model is transferred to practical tasks involving real objects scanned from the physical environment. In this paper, our empirical experiments show that synthetic-to-real domain shift significantly degrades the alignment of point cloud with their associated text embeddings in the 3D VLM latent space, hindering downstream performance. To address this, we propose a novel methodology called SODA which improves the detection of OOD point clouds through a neighborhood-based score propagation scheme. SODA is inference-based, requires no additional model training, and achieves state-of-the-art performance over existing approaches across datasets and problem settings.

Publisher Copyright

This research / project is supported by the Agency for Science, Technology and Research - Manufacturing, Trade, and Connectivity Programmatic Fund
Grant Reference no. : M23L7b0021

This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: https://doi.org/10.1007/978-3-032-05962-8_4

https://oar.a-star.edu.sg/communities-collections/articles/22233

arXiv:2506.21892

Institute for Infocomm Research