6 citations · 11 across the 4 of their papers we have counts for
8 papers
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…
Representation Learning Through Latent Canonicalizations
Or Litany, Ari Morcos, Srinath Sridhar +2
We seek to learn a representation on a large annotated data source that generalizes to a target domain using limited new supervision. Many prior approaches to this problem have foc…
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…