Conceitos essenciais
Geometrically-driven aggregation of vision-language model representations can effectively improve the quality of zero-shot 3D point cloud understanding across various downstream tasks.
Resumo
The content presents a novel approach called GeoZe, which is the first training-free technique that leverages the geometric structure of 3D point clouds to enhance the quality of transferred vision-language model (VLM) representations for zero-shot 3D understanding.
Key highlights:
- Existing methods for zero-shot 3D understanding directly map VLM representations from 2D pixels to 3D points, overlooking the inherent geometric structure of point clouds.
- GeoZe performs a geometrically-driven aggregation of VLM representations, first at the local level by considering neighboring points, and then at the global level by considering similar geometric structures across the point cloud.
- GeoZe introduces the concept of VLM representation anchors to preserve the integrity of the original VLM representations during the aggregation process.
- GeoZe is evaluated on three downstream tasks (classification, part segmentation, semantic segmentation) across various synthetic and real-world datasets, outperforming state-of-the-art methods.
- Ablation studies demonstrate the effectiveness of GeoZe's different components, including local/global aggregation and the use of geometric features.
Estatísticas
"VLM representations should exhibit local smoothness and global consistency when their geometric structures are similar."
"GeoZe consistently outperforms the considered baseline methods by a significant margin in a total of nine experiments."
Citações
"Unlike previous methods that employ naive pooling operations to transfer and aggregate VLM representations from images to 3D points [9, 20, 38], GeoZe harnesses both local and global structural information to enable geometric consistency of VLM representations across the point cloud."
"To achieve this, we introduce the concept of VLM representation anchors. These anchors serve to correct potential offsets that may arise during the aggregation process, thereby preserving the integrity of the original representations."