Diff-OP3D: Bridging 2D Diffusion for Open Pose 3D Zero-Shot Classification

With the explosive 3D data growth, the urgency of utilizing zero-shot learning to facilitate data labeling becomes evident. Recently, the methods via transferring Contrastive Language-Image Pre-training (CLIP) to 3D vision have made great progress in the 3D zero-shot classification task. However, these methods primarily focus on aligned pose 3D objects (ap-3os), overlooking the recognition of 3D objects with open poses (op-3os) typically encountered in real-world scenarios, such as an overturned chair or a lying teddy bear. To this end, we propose a more challenging benchmark for 3D open-pose zero-shot classification. Echoing our benchmark, we design a concise angle-refinement mechanism that automatically optimizes one ideal pose as well as classifies these op-3os. Furthermore, we make a first attempt to bridge 2D pre-trained diffusion model as a classifer to 3D zero-shot classification without any additional training. Such 2D diffusion to 3D objects proves vital in improving zero-shot classification for both ap-3os and op-3os. Our model notably improves by 3.5% and 15.8% on ModelNet10$^{\ddag}$ and McGill$^{\ddag}$ open pose benchmarks, respectively, and surpasses the current state-of-the-art by 6.8% on the aligned pose ModelNet10, affirming diffusion's efficacy in 3D zero-shot tasks.