In this review, we survey various methods for point cloud completion, focusing particularly on deep-learning-based approaches developed in recent years. We also discuss the future challenges and directions in this field. Based on the defined classification criteria, we elaborate on the advantages and...
This paper presents PCDreamer, a novel method for point cloud completion. Traditional methods typically extract features from partial point clouds to predict missing regions, but the large solution space often leads to unsatisfactory results. More recent approaches have started to use images as extra ...
The attention mechanism has significantly progressed in various point cloud tasks. Benefiting from its significant competence in capturing long-range dependencies, research in point cloud completion has achieved promising results. However, the typically disordered point cloud data features complicated non-Eucl...
deep-learningtransformerspytorch3dvisionpoint-cloud-completioniccv2021tpami2023 UpdatedJul 22, 2024 Python A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion diffusion-modelpoint-cloud-completion UpdatedApr 14, 2024 Python ...
Existing point cloud completion methods focus more on synthetic data or require separate modeling on each class of objects, which struggle to meet the inherent requirements of the completion task for real-world indoor scenes containing multi-class objects with significant pose variations. In this ...
Multi-Scale Point Cloud Generation,以coarse-to-fine的方式恢复点云。 4.现有的benchmarks不足,提出两个新的benchmarks——任务-点云的上采样和补全、目标类别-55类、多视点-所有可能的viewpoint、不完整程度-25%到75%。数据集 PCN 和 KITTI。 2. Related Work 3D Shape Transformers 3. Approach 3.1 Set...
Point cloud completionIn this paper, we propose a self-supervised learning method of point cloud completion for indoor scenes. Considering the limited view of single-view image and the time-consuming and labor-intensive acquisition of multi-view images, we take panoramas as input, which makes the...
Wen, “Style-based point generator with adversarial rendering for point cloud completion,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2021, pp. 4619–4628. ^X. Wen, Z. Han, Y.-P. Cao, P. Wan, W. Zheng, and Y.-S. Liu, “...
Pcn: Point completion network WangD. et al. Mutual information maximization based similarity operation for 3D point cloud completion network IEEE Signal Process. Lett. (2022)View more references Cited by (6) Regional dynamic point cloud completion network 2024, Pattern Recognition Letters Show abstract...
Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. In contrast to previous approaches, we present Partial2Complete (P2C), the first self-super...