The Odd-One-Out anomaly detection problem is an emerging research direction aimed at identifying visually distinct instances within multi-object 3D scenes. Unlike traditional anomaly detection tasks, which rely on predefined notions of normality or anomaly, the Odd-One-Out task is inherently contextual. An object's status as "anomalous" is determined not by global priors but by its dissimilarity to co-occurring objects in the same scene. This contextual framing introduces significant modeling challenges, requiring both spatial understanding of individual objects and relational reasoning to capture how objects relate to each other. This thesis explores how to learn context-aware object representations for 3D anomaly detection. We begin by reconstructing 3D voxel grids for each scene using multi-view 2D feature maps, which are backprojected using known camera intrinsics and extrinsics.