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.

通过在 3D 视觉领域运用 Contrastive Language-Image Pre-training (CLIP) 方法，本论文提出了一个更具挑战性的 3D 开放姿态零样本分类基准，并通过设计一个简洁的角度优化机制以及将 2D 预训练扩散模型应用于 3D 零样本分类，成功提高了模型性能，验证了扩散方法在 3D 零样本任务中的有效性。