Unpaired Point Cloud Completion on Real Scans using Adversarial Training
As 3D scanning solutions become increasingly popular, several deep learning setups have been developed geared towards that task of scan completion, i.e., plausibly filling in regions there were missed in the raw scans. These methods, however, largely rely on supervision in the form of paired training data, i.e., partial scans with corresponding desired completed scans. While these methods have been successfully demonstrated on synthetic data, the approaches cannot be directly used on real scans in absence of suitable paired training data. We develop a first approach that works directly on input point clouds, does not require paired training data, and hence can directly be applied to real scans for scan completion. We evaluate the approach qualitatively on several real-world datasets (ScanNet, Matterport, KITTI), quantitatively on 3D-EPN shape completion benchmark dataset, and demonstrate realistic completions under varying levels of incompleteness.
Code (2)
Tasks
Point Cloud CompletionSimilar Papers 제목 키워드 기반
RefComp: A Reference-guided Unified Framework for Unpaired Point Cloud Completion
The unpaired point cloud completion task aims to complete a partial point cloud by using models trained with no ground truth. Existing unpaired point cloud completion methods are class-aware, i.e., a separate model is ne…
Point Cloud CompletionTranslationUnsupervised Point Cloud Completion through Unbalanced Optimal Transport
Unpaired point cloud completion explores methods for learning a completion map from unpaired incomplete and complete point cloud data. In this paper, we propose a novel approach for unpaired point cloud completion using …
Point Cloud CompletionReconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion
Real-world sensors often produce incomplete, irregular, and noisy point clouds, making point cloud completion increasingly important. However, most existing completion methods rely on large paired datasets for training, …
Point Cloud CompletionLearning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach
Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of incomplete and complete point clou…
Point Cloud CompletionSelf-supervised Point Cloud Completion on Real Traffic Scenes via Scene-concerned Bottom-up Mechanism
Real scans always miss partial geometries of objects due to the self-occlusions, external-occlusions, and limited sensor resolutions. Point cloud completion aims to refer the complete shapes for incomplete 3D scans of ob…
Point Cloud Completion