6 citations · 11 across the 4 of their papers we have counts for
7 papers · 1 filter
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects
Rahul Sajnani, AadilMehdi Sanchawala, Krishna Murthy Jatavallabhula +2
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images. Canonical shape reconstruction, estimating 3D object shape in…
Pix2Surf: Learning Parametric 3D Surface Models of Objects from Images
Jiahui Lei, Srinath Sridhar, Paul Guerrero +3
We investigate the problem of learning to generate 3D parametric surface representations for novel object instances, as seen from one or more views. Previous work on learning shape…
CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations
Davis Rempe, Tolga Birdal, Yongheng Zhao +3
We propose CaSPR, a method to learn object-centric Canonical Spatiotemporal Point Cloud Representations of dynamically moving or evolving objects. Our goal is to enable information…
Continuous Geodesic Convolutions for Learning on 3D Shapes
Zhangsihao Yang, Or Litany, Tolga Birdal +2
The majority of descriptor-based methods for geometric processing of non-rigid shape rely on hand-crafted descriptors. Recently, learning-based techniques have been shown effective…
Multiview Aggregation for Learning Category-Specific Shape Reconstruction
Srinath Sridhar, Davis Rempe, Julien Valentin +2
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances. Most approaches for…
Learning Generalizable Physical Dynamics of 3D Rigid Objects
Davis Rempe, Srinath Sridhar, He Wang +1
Humans have a remarkable ability to predict the effect of physical interactions on the dynamics of objects. Endowing machines with this ability would allow important applications i…