3 research outputs found
ACL-SPC: Adaptive Closed-Loop system for Self-Supervised Point Cloud Completion
Point cloud completion addresses filling in the missing parts of a partial
point cloud obtained from depth sensors and generating a complete point cloud.
Although there has been steep progress in the supervised methods on the
synthetic point cloud completion task, it is hardly applicable in real-world
scenarios due to the domain gap between the synthetic and real-world datasets
or the requirement of prior information. To overcome these limitations, we
propose a novel self-supervised framework ACL-SPC for point cloud completion to
train and test on the same data. ACL-SPC takes a single partial input and
attempts to output the complete point cloud using an adaptive closed-loop (ACL)
system that enforces the output same for the variation of an input. We evaluate
our proposed ACL-SPC on various datasets to prove that it can successfully
learn to complete a partial point cloud as the first self-supervised scheme.
Results show that our method is comparable with unsupervised methods and
achieves superior performance on the real-world dataset compared to the
supervised methods trained on the synthetic dataset. Extensive experiments
justify the necessity of self-supervised learning and the effectiveness of our
proposed method for the real-world point cloud completion task. The code is
publicly available from https://github.com/Sangminhong/ACL-SPC_PyTorchComment: Published at CVPR 202
ICF-SRSR: Invertible scale-Conditional Function for Self-Supervised Real-world Single Image Super-Resolution
Single image super-resolution (SISR) is a challenging ill-posed problem that
aims to up-sample a given low-resolution (LR) image to a high-resolution (HR)
counterpart. Due to the difficulty in obtaining real LR-HR training pairs,
recent approaches are trained on simulated LR images degraded by simplified
down-sampling operators, e.g., bicubic. Such an approach can be problematic in
practice because of the large gap between the synthesized and real-world LR
images. To alleviate the issue, we propose a novel Invertible scale-Conditional
Function (ICF), which can scale an input image and then restore the original
input with different scale conditions. By leveraging the proposed ICF, we
construct a novel self-supervised SISR framework (ICF-SRSR) to handle the
real-world SR task without using any paired/unpaired training data.
Furthermore, our ICF-SRSR can generate realistic and feasible LR-HR pairs,
which can make existing supervised SISR networks more robust. Extensive
experiments demonstrate the effectiveness of the proposed method in handling
SISR in a fully self-supervised manner. Our ICF-SRSR demonstrates superior
performance compared to the existing methods trained on synthetic paired images
in real-world scenarios and exhibits comparable performance compared to
state-of-the-art supervised/unsupervised methods on public benchmark datasets