56 citations · 81 across the 5 of their papers we have counts for
7 papers · 1 filter
Potential Convolution: Embedding Point Clouds into Potential Fields
Dengsheng Chen, Haowen Deng, Jun Li +3
Recently, various convolutions based on continuous or discrete kernels for point cloud processing have been widely studied, and achieve impressive performance in many applications,…
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation
Haowen Deng, Mai Bui, Nassir Navab +3
In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life app…
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
Mai Bui, Tolga Birdal, Haowen Deng +4
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. In highl…
3D Local Features for Direct Pairwise Registration
Haowen Deng, Tolga Birdal, Slobodan Ilic
We present a novel, data driven approach for solving the problem of registration of two point cloud scans. Our approach is direct in the sense that a single pair of corresponding l…
3D Point Capsule Networks
Yongheng Zhao, Tolga Birdal, Haowen Deng +1
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. 3D capsule…
PPF-FoldNet: Unsupervised Learning of Rotation Invariant 3D Local Descriptors
Haowen Deng, Tolga Birdal, Slobodan Ilic
We present PPF-FoldNet for unsupervised learning of 3D local descriptors on pure point cloud geometry. Based on the folding-based auto-encoding of well known point pair features, P…
PPFNet: Global Context Aware Local Features for Robust 3D Point Matching
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. PPFNet learn…