Y Zeng, C Jiang, J Mao, J Han, C Ye… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract Contrastive Language-Image Pre-training, benefiting from large-scale unlabeled text-image pairs, has demonstrated great performance in open-world vision understanding …
Closed-set 3D perception models trained on only a pre-defined set of object categories can be inadequate for safety critical applications such as autonomous driving where new object …
J Zhang, R Dong, K Ma - Proceedings of the IEEE/CVF …, 2023 - openaccess.thecvf.com
Training a 3D scene understanding model requires complicated human annotations, which are laborious to collect and result in a model only encoding close-set object semantics. In …
Large-scale pre-trained models have shown promising open-world performance for both vision and language tasks. However, their transferred capacity on 3D point clouds is still …
Recently, zero-shot and few-shot learning via Contrastive Vision-Language Pre-training (CLIP) have shown inspirational performance on 2D visual recognition, which learns to …
R Ding, J Yang, C Xue, W Zhang… - Proceedings of the …, 2023 - openaccess.thecvf.com
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space. The recent breakthrough of 2D open-vocabulary …
Y Liu, K Zhu, G Wu, Y Ren, B Liu… - Proceedings of the …, 2023 - openaccess.thecvf.com
Reconstructing 3D vehicles from noisy and sparse partial point clouds is of great significance to autonomous driving. Most existing 3D reconstruction methods cannot be …
L Xue, N Yu, S Zhang… - Proceedings of the …, 2024 - openaccess.thecvf.com
Recent advancements in multimodal pre-training have shown promising efficacy in 3D representation learning by aligning multimodal features across 3D shapes their 2D …