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Found 10 papers, 5 papers with code
CTGAN: Semantic-guided Conditional Texture Generator for 3D Shapes
The entertainment industry relies on 3D visual content to create immersive experiences, but traditional methods for creating textured 3D models can be time-consuming and subjective.
Layout-guided Indoor Panorama Inpainting with Plane-aware Normalization
Specifically, we take the low-level layout edges estimated from the input panorama as a prior to guide the inpainting model for recovering the global indoor structure.
Sampling Neural Radiance Fields for Refractive Objects
To tackle this challenging problem, the refractive index of a scene is reconstructed from silhouettes.
GPR-Net: Multi-view Layout Estimation via a Geometry-aware Panorama Registration Network
The major improvement over PSMNet comes from a novel Geometry-aware Panorama Registration Network or GPR-Net that effectively tackles the wide baseline registration problem by exploiting the layout geometry and computing fine-grained correspondences on the layout boundaries, instead of the global pixel-space.
Instance-aware Image Colorization
Previous methods leverage the deep neural network to map input grayscale images to plausible color outputs directly.
Ranked #2 on
Point-interactive Image Colorization
on CUB-200-2011
(using extra training data)
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.
Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
Recent approaches for predicting layouts from 360 panoramas produce excellent results.
DuLa-Net: A Dual-Projection Network for Estimating Room Layouts from a Single RGB Panorama
We present a deep learning framework, called DuLa-Net, to predict Manhattan-world 3D room layouts from a single RGB panorama.
Self-Supervised Learning of Depth and Camera Motion from 360° Videos
We propose a novel self-supervised learning approach for predicting the omnidirectional depth and camera motion from a 360{\deg} video.
SmartAnnotator: An Interactive Tool for Annotating RGBD Indoor Images
Further, as more scenes are annotated, the system makes better suggestions based on structural and geometric priors learns from the previous annotation sessions.
