Search Results for author:
Found 12 papers, 3 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.
Self-Training and Adversarial Background Regularization for Unsupervised Domain Adaptive One-Stage Object Detection
Experimental results show that our approach effectively improves the performance of the one-stage object detection in unsupervised domain adaptation setting.
Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation
Unsupervised domain adaptation seeks to adapt the model trained on the source domain to the target domain.
Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation
To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation.
Semi-Supervised Image Classification
Unsupervised Domain Adaptation
Learning User Preferences and Understanding Calendar Contexts for Event Scheduling
With online calendar services gaining popularity worldwide, calendar data has become one of the richest context sources for understanding human behavior.
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database.
