Open-Pose 3D Zero-Shot Learning: Benchmark and Challenges
arxiv(2023)
Abstract
With the explosive 3D data growth, the urgency of utilizing zero-shot
learning to facilitate data labeling becomes evident. Recently, methods
transferring language or language-image pre-training models like Contrastive
Language-Image Pre-training (CLIP) to 3D vision have made significant progress
in the 3D zero-shot classification task. These methods primarily focus on 3D
object classification with an aligned pose; such a setting is, however, rather
restrictive, which overlooks the recognition of 3D objects with open poses
typically encountered in real-world scenarios, such as an overturned chair or a
lying teddy bear. To this end, we propose a more realistic and challenging
scenario named open-pose 3D zero-shot classification, focusing on the
recognition of 3D objects regardless of their orientation. First, we revisit
the current research on 3D zero-shot classification, and propose two benchmark
datasets specifically designed for the open-pose setting. We empirically
validate many of the most popular methods in the proposed open-pose benchmark.
Our investigations reveal that most current 3D zero-shot classification models
suffer from poor performance, indicating a substantial exploration room towards
the new direction. Furthermore, we study a concise pipeline with an iterative
angle refinement mechanism that automatically optimizes one ideal angle to
classify these open-pose 3D objects. In particular, to make validation more
compelling and not just limited to existing CLIP-based methods, we also pioneer
the exploration of knowledge transfer based on Diffusion models. While the
proposed solutions can serve as a new benchmark for open-pose 3D zero-shot
classification, we discuss the complexities and challenges of this scenario
that remain for further research development. The code is available publicly at
https://github.com/weiguangzhao/Diff-OP3D.
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