161 citations · 483 across the 28 of their papers we have counts for
6 papers · 1 filter
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…
MV-YOLO: Motion Vector-aided Tracking by Semantic Object Detection
Saeed Ranjbar Alvar, Ivan V. Bajić
Object tracking is the cornerstone of many visual analytics systems. While considerable progress has been made in this area in recent years, robust, efficient, and accurate trackin…
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…
Adaptive Non-Rigid Inpainting of 3D Point Cloud Geometry
Chinthaka Dinesh, Ivan V. Bajic, Gene Cheung
In this letter, we introduce several algorithms for geometry inpainting of 3D point clouds with large holes. The algorithms are examplar-based: hole filling is performed iterativel…
Near-Lossless Deep Feature Compression for Collaborative Intelligence
Hyomin Choi, Ivan V. Bajic
Collaborative intelligence is a new paradigm for efficient deployment of deep neural networks across the mobile-cloud infrastructure. By dividing the network between the mobile and…
Deep feature compression for collaborative object detection
Hyomin Choi, Ivan V. Bajic
Recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mo…