  In the field of 3D scene understanding, 3D scene graphs have emerged as a new
scene representation that combines geometric and semantic information about
objects and their relationships. However, learning semantic 3D scene graphs in
a fully supervised manner is inherently difficult as it requires not only
object-level annotations but also relationship labels. While pre-training
approaches have helped to boost the performance of many methods in various
fields, pre-training for 3D scene graph prediction has received little
attention. Furthermore, we find in this paper that classical contrastive point
cloud-based pre-training approaches are ineffective for 3D scene graph
learning. To this end, we present SGRec3D, a novel self-supervised pre-training
method for 3D scene graph prediction. We propose to reconstruct the 3D input
scene from a graph bottleneck as a pretext task. Pre-training SGRec3D does not
require object relationship labels, making it possible to exploit large-scale
3D scene understanding datasets, which were off-limits for 3D scene graph
learning before. Our experiments demonstrate that in contrast to recent point
cloud-based pre-training approaches, our proposed pre-training improves the 3D
scene graph prediction considerably, which results in SOTA performance,
outperforming other 3D scene graph models by +10% on object prediction and +4%
on relationship prediction. Additionally, we show that only using a small
subset of 10% labeled data during fine-tuning is sufficient to outperform the
same model without pre-training.
