258 citations · 829 across the 41 of their papers we have counts for
6 papers · 1 filter
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation
Zirui Wang, Shuda Li, Henry Howard-Jenkins +2
We present FlowNet3D++, a deep scene flow estimation network. Inspired by classical methods, FlowNet3D++ incorporates geometric constraints in the form of point-to-plane distance a…
GroSS: Group-Size Series Decomposition for Grouped Architecture Search
Henry Howard-Jenkins, Yiwen Li, Victor A. Prisacariu
We present a novel approach which is able to explore the configuration of grouped convolutions within neural networks. Group-size Series (GroSS) decomposition is a mathematical for…
Domain-invariant Stereo Matching Networks
Feihu Zhang, Xiaojuan Qi, Ruigang Yang +3
State-of-the-art stereo matching networks have difficulties in generalizing to new unseen environments due to significant domain differences, such as color, illumination, contrast,…
Thinking Outside the Box: Generation of Unconstrained 3D Room Layouts
Henry Howard-Jenkins, Shuda Li, Victor Prisacariu
We propose a method for room layout estimation that does not rely on the typical box approximation or Manhattan world assumption. Instead, we reformulate the geometry inference pro…
GA-Net: Guided Aggregation Net for End-to-end Stereo Matching
Feihu Zhang, Victor Prisacariu, Ruigang Yang +1
In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose…
DSConv: Efficient Convolution Operator
Marcelo Gennari, Roger Fawcett, Victor Adrian Prisacariu
Quantization is a popular way of increasing the speed and lowering the memory usage of Convolution Neural Networks (CNNs). When labelled training data is available, network weights…