SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation
Journal:
arXiv
Published Date:
Jun 27, 2025
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.
