Abstract
Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding tasks. However, due to the limited Text-3D data pairs, adapting the success of 2D Vision-Language Models (VLM) to the 3D space remains an open problem. Existing works that leverage VLM for 3D understanding generally resort to constructing intermediate 2D representations for the 3D data, but at the cost of losing 3D geometry information. To take a step toward open-world 3D vision understanding, we propose Contrastive Language-Image-Point Cloud Pretraining (CLIP^2) to directly learn the transferable 3D point cloud representation in realistic scenarios with a novel proxy alignment mechanism. Specifically, we exploit naturally-existed correspondences in 2D and 3D scenarios, and build well-aligned and instance-based text-image-point proxies from those complex scenarios. On top of that, we propose a cross-modal contrastive objective to learn semantic and instance-level aligned point cloud representation. Experimental results on both indoor and outdoor scenarios show that our learned 3D representation has great transfer ability in downstream tasks, including zero-shot and few-shot 3D recognition, which boosts the state-of-the-art methods by large margins. Furthermore, we provide analyses of the capability of different representations in real scenarios and present the optional ensemble scheme.
Abstract (translated)
Contrastive Language-Image Pre-training利用大规模未标记文本图像 pairs 展示了在开放世界视觉理解任务中的良好表现。然而,由于文本-3D数据 pairs 有限,将2D视觉-语言模型(VLM)在3D空间中的成功适应仍然是一个开放性问题。现有的工作利用VLM为3D理解而使用,通常只能构建2D中间表示,但却失去了3D几何信息。为了迈向开放世界3D视觉理解,我们提出了Contrastive Language-Image-Point Cloud Pretraining(CLIP^2),通过一种新的代理对齐机制,在真实的场景下直接学习可转移的3D点云表示。具体来说,我们利用2D和3D场景中的自然对应关系,从这些复杂的场景中构建对齐的文本-图像-点代理。此外,我们提出了一个跨modalContrastive目标,以学习语义和实例级别的对齐点云表示。在室内和室外场景中的实验结果显示,我们学习到的3D表示在后续任务中具有很强的转移能力,包括零和经验3D识别,这极大地提高了现有方法。此外,我们提供了不同表示在真实场景下的能力分析,并提出了可选的集成方案。
URL
https://arxiv.org/abs/2303.12417
