  Current 3D self-supervised learning methods of 3D scenes face a data desert
issue, resulting from the time-consuming and expensive collecting process of 3D
scene data. Conversely, 3D shape datasets are easier to collect. Despite this,
existing pre-training strategies on shape data offer limited potential for 3D
scene understanding due to significant disparities in point quantities. To
tackle these challenges, we propose Shape2Scene (S2S), a novel method that
learns representations of large-scale 3D scenes from 3D shape data. We first
design multiscale and high-resolution backbones for shape and scene level 3D
tasks, i.e., MH-P (point-based) and MH-V (voxel-based). MH-P/V establishes
direct paths to highresolution features that capture deep semantic information
across multiple scales. This pivotal nature makes them suitable for a wide
range of 3D downstream tasks that tightly rely on high-resolution features. We
then employ a Shape-to-Scene strategy (S2SS) to amalgamate points from various
shapes, creating a random pseudo scene (comprising multiple objects) for
training data, mitigating disparities between shapes and scenes. Finally, a
point-point contrastive loss (PPC) is applied for the pre-training of MH-P/V.
In PPC, the inherent correspondence (i.e., point pairs) is naturally obtained
in S2SS. Extensive experiments have demonstrated the transferability of 3D
representations learned by MH-P/V across shape-level and scene-level 3D tasks.
MH-P achieves notable performance on well-known point cloud datasets (93.8% OA
on ScanObjectNN and 87.6% instance mIoU on ShapeNetPart). MH-V also achieves
promising performance in 3D semantic segmentation and 3D object detection.
