  Learning to build 3D scene graphs is essential for real-world perception in a
structured and rich fashion. However, previous 3D scene graph generation
methods utilize a fully supervised learning manner and require a large amount
of entity-level annotation data of objects and relations, which is extremely
resource-consuming and tedious to obtain. To tackle this problem, we propose
3D-VLAP, a weakly-supervised 3D scene graph generation method via
Visual-Linguistic Assisted Pseudo-labeling. Specifically, our 3D-VLAP exploits
the superior ability of current large-scale visual-linguistic models to align
the semantics between texts and 2D images, as well as the naturally existing
correspondences between 2D images and 3D point clouds, and thus implicitly
constructs correspondences between texts and 3D point clouds. First, we
establish the positional correspondence from 3D point clouds to 2D images via
camera intrinsic and extrinsic parameters, thereby achieving alignment of 3D
point clouds and 2D images. Subsequently, a large-scale cross-modal
visual-linguistic model is employed to indirectly align 3D instances with the
textual category labels of objects by matching 2D images with object category
labels. The pseudo labels for objects and relations are then produced for
3D-VLAP model training by calculating the similarity between visual embeddings
and textual category embeddings of objects and relations encoded by the
visual-linguistic model, respectively. Ultimately, we design an edge
self-attention based graph neural network to generate scene graphs of 3D point
cloud scenes. Extensive experiments demonstrate that our 3D-VLAP achieves
comparable results with current advanced fully supervised methods, meanwhile
significantly alleviating the pressure of data annotation.
