Deep semantic grounding of language in 3D point cloud representations

Develop architectures and training objectives for 3D point cloud representation learning that move beyond shallow feature alignment (e.g., linear probing) to achieve deep semantic grounding with language, enabling the learned embeddings to comprehend and respond to nuanced, indirect, or compositional linguistic descriptions.

Background

The paper currently evaluates language alignment via a linear probing translator that maps Concerto’s self-supervised point cloud features to CLIP’s language space, which the authors describe as a shallow alignment designed primarily for evaluation without affecting pretraining. While this shows promising zero-shot segmentation capability, the authors argue that such shallow alignment is insufficient for real-world applications.

They identify a next step: designing architectures and training objectives that enable point cloud representations to encode deeper semantic understanding aligned with language. Specifically, they call for capabilities to handle nuanced, indirect, or compositional linguistic descriptions—capabilities beyond the current linear probing setup—explicitly noting that achieving such deep grounding remains an open challenge.