Search Results for author:
Found 5 papers, 2 papers with code
DDM-NET: End-to-end learning of keypoint feature Detection, Description and Matching for 3D localization
In this paper, we propose an end-to-end framework that jointly learns keypoint detection, descriptor representation and cross-frame matching for the task of image-based 3D localization.
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
The most popular and accessible 3D representation, i. e., point clouds, involves discrete samples of the underlying continuous 3D surface.
Ranked #5 on
3D Point Cloud Linear Classification
on ModelNet40
(using extra training data)
3D Point Cloud Classification
3D Point Cloud Linear Classification
+4
Learning View Selection for 3D Scenes
Efficient 3D space sampling to represent an underlying3D object/scene is essential for 3D vision, robotics, and be-yond.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.
Pano Popups: Indoor 3D Reconstruction with a Plane-Aware Network
In this work we present a method to train a plane-aware convolutional neural network for dense depth and surface normal estimation as well as plane boundaries from a single indoor $360^\circ$ image.
