Search Results for author:
Found 8 papers, 4 papers with code
SceneScript: Reconstructing Scenes With An Autoregressive Structured Language Model
We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach.
OrienterNet: Visual Localization in 2D Public Maps with Neural Matching
We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the same 2D semantic maps that humans use.
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism.
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
We let a feature encoding network and image reconstruction network compete with each other, such that the feature encoder tries to impede the image reconstruction with its generated descriptors, while the reconstructor tries to recover the input image from the descriptors.
UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision
In this paper, we explore how three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework, which is trained without the need of training data with point to point correspondences.
FSA-Net: Learning Fine-Grained Structure Aggregation for Head Pose Estimation From a Single Image
Our method is based on regression and feature aggregation.
Ranked #12 on
Head Pose Estimation
on BIWI
DeepCD: Learning Deep Complementary Descriptors for Patch Representations
This paper presents the DeepCD framework which learns a pair of complementary descriptors jointly for a patch by employing deep learning techniques.
Accumulated Stability Voting: A Robust Descriptor From Descriptors of Multiple Scales
Experiments on popular benchmarks demonstrate the effectiveness of our descriptors and their superiority to the state-of-the-art descriptors.
