no code implementations • 5 Apr 2021 • Dengsheng Chen, Haowen Deng, Jun Li, Duo Li, Yao Duan, Kai Xu
In this work, rather than defining a continuous or discrete kernel, we directly embed convolutional kernels into the learnable potential fields, giving rise to potential convolution.
1 code implementation • 20 Dec 2020 • Haowen Deng, Mai Bui, Nassir Navab, Leonidas Guibas, Slobodan Ilic, Tolga Birdal
For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify.
2 code implementations • ECCV 2020 • Mai Bui, Tolga Birdal, Haowen Deng, Shadi Albarqouni, Leonidas Guibas, Slobodan Ilic, Nassir Navab
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses.
no code implementations • CVPR 2019 • Haowen Deng, Tolga Birdal, Slobodan Ilic
Our extensive quantitative and qualitative experiments suggests that our approach outperforms the state of the art in challenging real datasets of pairwise registration and that augmenting the keypoints with local pose information leads to better generalization and a dramatic speed-up.
2 code implementations • CVPR 2019 • Yongheng Zhao, Tolga Birdal, Haowen Deng, Federico Tombari
In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Ranked #4 on 3D Object Classification on ModelNet40
2 code implementations • ECCV 2018 • Haowen Deng, Tolga Birdal, Slobodan Ilic
We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry.
Ranked #12 on Point Cloud Registration on 3DMatch Benchmark
1 code implementation • CVPR 2018 • Haowen Deng, Tolga Birdal, Slobodan Ilic
We present PPFNet - Point Pair Feature NETwork for deeply learning a globally informed 3D local feature descriptor to find correspondences in unorganized point clouds.
Ranked #14 on Point Cloud Registration on 3DMatch Benchmark