32 citations · 54 across the 11 of their papers we have counts for
7 papers · 1 filter
Point Cloud Sampling via Graph Balancing and Gershgorin Disc Alignment
Chinthaka Dinesh, Gene Cheung, Ivan Bajic
3D point cloud (PC) -- a collection of discrete geometric samples of a physical object's surface -- is typically large in size, which entails expensive subsequent operations like v…
PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks
Alon Harell, Richard Jones, Stephen Makonin +1
Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption using only the building's smart meter. Most curre…
3D Point Cloud Super-Resolution via Graph Total Variation on Surface Normals
Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic
Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Using a low-cost 3D scanner to acquire data means that point clouds ar…
Wavenilm: A causal neural network for power disaggregation from the complex power signal
Alon Harell, Stephen Makonin, Ivan V. Bajić
Non-intrusive load monitoring (NILM) helps meet energy conservation goals by estimating individual appliance power usage from a single aggregate measurement. Deep neural networks h…
3D Point Cloud Denoising via Bipartite Graph Approximation and Reweighted Graph Laplacian
Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic
Point cloud is a collection of 3D coordinates that are discrete geometric samples of an object's 2D surfaces. Imperfection in the acquisition process means that point clouds are of…
Fast 3D Point Cloud Denoising via Bipartite Graph Approximation & Total Variation
Chinthaka Dinesh, Gene Cheung, Ivan V. Bajic +1
Acquired 3D point cloud data, whether from active sensors directly or from stereo-matching algorithms indirectly, typically contain non-negligible noise. To address the point cloud…