Contrastive learning has emerged as a promising paradigm for 3D open-world
understanding ie aligning point cloud representation to image and text embedding space …

Contrastive learning has emerged as a promising paradigm for 3D open-world
understanding, jointly with text, image, and point cloud. In this paper, we introduce …

Abstract Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled
text-image pairs, has demonstrated great performance in open-world vision understanding …

3D Shape represented as point cloud has achieve advancements in multimodal pre-training
to align image and language descriptions, which is curial to object identification …

Abstract 3D visual language reasoning plays an important role in effective human-computer
interaction. The current approaches for 3D visual reasoning are task-specific, and lack pre …

Recent advancements in vision-language pre-training (eg, CLIP) have enabled 2D vision
models to benefit from language supervision. However, the joint representation learning of …

We propose a lightweight and scalable Regional Point-Language Contrastive learning
framework namely RegionPLC for open-world 3D scene understanding aiming to identify …

The rapid advancement of deep learning models is often attributed to their ability to leverage
massive training data. In contrast such privilege has not yet fully benefited 3D deep learning …

Recent advances in Vision and Language Models (VLMs) have improved open-world 3D
representation, facilitating 3D zero-shot capability in unseen categories. Existing open-world …

3D Vision-Language Pre-training (3D-VLP) aims to provide a pre-train model which can
bridge 3D scenes with natural language, which is an important technique for embodied …