5 papers
A Real-Time Event-Based Normal Flow Estimator
Dehao Yuan, Cornelia Fermüller
This paper presents a real-time, asynchronous, event-based normal flow estimator. It follows the same algorithm as Learning Normal Flow Directly From Event Neighborhoods, but with…
Learning Normal Flow Directly From Event Neighborhoods
Dehao Yuan, Levi Burner, Jiayi Wu +4
Event-based motion field estimation is an important task. However, current optical flow methods face challenges: learning-based approaches, often frame-based and relying on CNNs, l…
A Linear Time and Space Local Point Cloud Geometry Encoder via Vectorized Kernel Mixture (VecKM)
Dehao Yuan, Cornelia Fermüller, Tahseen Rabbani +2
We propose VecKM, a local point cloud geometry encoder that is descriptive and efficient to compute. VecKM leverages a unique approach by vectorizing a kernel mixture to represent…
Decodable and Sample Invariant Continuous Object Encoder
Dehao Yuan, Furong Huang, Cornelia Fermüller +1
We propose Hyper-Dimensional Function Encoding (HDFE). Given samples of a continuous object (e.g. a function), HDFE produces an explicit vector representation of the given object,…
Gluing Neural Networks Symbolically Through Hyperdimensional Computing
Peter Sutor, Dehao Yuan, Douglas Summers-Stay +2
Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learn…