384 citations · 412 across the 8 of their papers we have counts for
10 papers
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…
ERF: Explicit Radiance Field Reconstruction From Scratch
Samir Aroudj, Steven Lovegrove, Eddy Ilg +3
We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model.…
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.…
FroDO: From Detections to 3D Objects
Kejie Li, Martin Rünz, Meng Tang +8
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects.…
Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg +4
Efficiently reconstructing complex and intricate surfaces at scale is a long-standing goal in machine perception. To address this problem we introduce Deep Local Shapes (DeepLS), a…