5 citations · 12 across the 8 of their papers we have counts for
13 papers
Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
Gil Avraham, Julian Straub, Tianwei Shen +7
This paper presents a framework that combines traditional keypoint-based camera pose optimization with an invertible neural rendering mechanism. Our proposed 3D scene representatio…
Self-supervised Neural Articulated Shape and Appearance Models
Fangyin Wei, Rohan Chabra, Lingni Ma +6
Learning geometry, motion, and appearance priors of object classes is important for the solution of a large variety of computer vision problems. While the majority of approaches ha…
LISA: Learning Implicit Shape and Appearance of Hands
Enric Corona, Tomas Hodan, Minh Vo +4
This paper proposes a do-it-all neural model of human hands, named LISA. The model can capture accurate hand shape and appearance, generalize to arbitrary hand subjects, provide de…
NinjaDesc: Content-Concealing Visual Descriptors via Adversarial Learning
Tony Ng, Hyo Jin Kim, Vincent Lee +7
In the light of recent analyses on privacy-concerning scene revelation from visual descriptors, we develop descriptors that conceal the input image content. In particular, we propo…
ODAM: Object Detection, Association, and Mapping using Posed RGB Video
Kejie Li, Daniel DeTone, Steven Chen +6
Localizing objects and estimating their extent in 3D is an important step towards high-level 3D scene understanding, which has many applications in Augmented Reality and Robotics.…
Scalable Scene Flow from Point Clouds in the Real World
Philipp Jund, Chris Sweeney, Nichola Abdo +2
Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to. A popular appro…