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

• URL: http://arxiv.org/abs/2506.21892v1
• Date: Fri, 27 Jun 2025 04:05:55 GMT
• Title: SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation
• Authors: Adam Goodge, Xun Xu, Bryan Hooi, Wee Siong Ng, Jingyi Liao, Yongyi Su, Xulei Yang,
• Abstract summary: We exploit advances in 3D vision-language models for OOD detection in point cloud objects.<n>A major challenge is that point cloud datasets are drastically smaller in size and object diversity than their image-based counterparts.<n>We propose a novel methodology called SODA which improves the detection of OOD point clouds through a neighborhood-based score propagation scheme.
• Score: 32.375671187101716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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