Results 1 to 10 of about 5,826,707 (172)
Masked Autoencoders for Point Cloud Self-supervised Learning [PDF]
European Conference on Computer Vision, 2022 As a promising scheme of self-supervised learning, masked autoencoding has significantly advanced natural language processing and computer vision. Inspired by this, we propose a neat scheme of masked autoencoders for point cloud self-supervised learning,
Yatian Pang +5 more
semanticscholar +1 more source
Geometric Transformer for Fast and Robust Point Cloud Registration [PDF]
Computer Vision and Pattern Recognition, 2022 We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult in low-overlap scenarios, showing great potential in registration. They
Zheng Qin +5 more
semanticscholar +1 more source
Stratified Transformer for 3D Point Cloud Segmentation [PDF]
Computer Vision and Pattern Recognition, 2022 3D point cloud segmentation has made tremendous progress in recent years. Most current methods focus on aggregating local features, but fail to directly model long-range dependencies.
Xin Lai +7 more
semanticscholar +1 more source
Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling [PDF]
Computer Vision and Pattern Recognition, 2021 We present Point-BERT, a new paradigm for learning Transformers to generalize the concept of BERT [8] to 3D point cloud. Inspired by BERT, we devise a Masked Point Modeling (MPM) task to pre-train point cloud Transformers. Specifically, we first divide a
Xumin Yu +5 more
semanticscholar +1 more source
PoinTr: Diverse Point Cloud Completion with Geometry-Aware Transformers [PDF]
IEEE International Conference on Computer Vision, 2021 Point clouds captured in real-world applications are of-ten incomplete due to the limited sensor resolution, single viewpoint, and occlusion. Therefore, recovering the complete point clouds from partial ones becomes an indispensable task in many ...
Xumin Yu +5 more
semanticscholar +1 more source
Diffusion Probabilistic Models for 3D Point Cloud Generation [PDF]
Computer Vision and Pattern Recognition, 2021 We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
Shitong Luo, Wei Hu
semanticscholar +1 more source
PointCLIP: Point Cloud Understanding by CLIP [PDF]
Computer Vision and Pattern Recognition, 2021 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 match images with their corresponding texts in open-vocabulary settings. However,
Renrui Zhang +8 more
semanticscholar +1 more source
PCT: Point cloud transformer [PDF]
Computational Visual Media, 2020 The irregular domain and lack of ordering make it challenging to design deep neural networks for point cloud processing. This paper presents a novel framework named Point Cloud Transformer (PCT) for point cloud learning.
Meng-Hao Guo +5 more
semanticscholar +1 more source
Self-Supervised Pretraining of 3D Features on any Point-Cloud [PDF]
IEEE International Conference on Computer Vision, 2021 Pretraining on large labeled datasets is a prerequisite to achieve good performance in many computer vision tasks like image recognition, video understanding etc.
Zaiwei Zhang +3 more
semanticscholar +1 more source
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding [PDF]
Computer Vision and Pattern Recognition, 2022 Manual annotation of large-scale point cloud dataset for varying tasks such as 3D object classification, segmentation and detection is often laborious owing to the irregular structure of point clouds.
Mohamed Afham +5 more
semanticscholar +1 more source