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Found 8 papers, 1 papers with code
TMO: Textured Mesh Acquisition of Objects with a Mobile Device by using Differentiable Rendering
We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses.
SelfTune: Metrically Scaled Monocular Depth Estimation through Self-Supervised Learning
Given these metric poses and monocular sequences, we propose a self-supervised learning method for the pre-trained supervised monocular depth networks to enable metrically scaled depth estimation.
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes
We present a novel approach for estimating depth from a monocular camera as it moves through complex and crowded indoor environments, e. g., a department store or a metro station.
Just a Few Points Are All You Need for Multi-View Stereo: A Novel Semi-Supervised Learning Method for Multi-View Stereo
We generate the spare ground truth of the DTU dataset for evaluation and extensive experiments verify that our SGT-MVSNet outperforms the state-of-the-art MVS methods on the sparse ground truth setting.
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
We present a novel algorithm for self-supervised monocular depth completion.
SAFENet: Self-Supervised Monocular Depth Estimation with Semantic-Aware Feature Extraction
In this paper, we propose SAFENet that is designed to leverage semantic information to overcome the limitations of the photometric loss.
Arbitrary Style Transfer using Graph Instance Normalization
Style transfer is the image synthesis task, which applies a style of one image to another while preserving the content.
Partial Domain Adaptation Using Graph Convolutional Networks
Although existing partial domain adaptation methods effectively down-weigh outliers' importance, they do not consider data structure of each domain and do not directly align the feature distributions of the same class in the source and target domains, which may lead to misalignment of category-level distributions.
