arXiv subjects

Rajrup Ghosh

Publications and source records attributed to Rajrup Ghosh.

4 recordsLinked to original sources

arXiv2026

GS-NFS: Bandwidth-adaptive Streaming of Dynamic Gaussian Splats and Point Clouds

Dynamic 3D Gaussian Splatting (3DGS) holds great promise as a 3D video streaming technology since it can represent complex 3D scenes with high fidelity. In this approach, every frame in a 3D video represents the environment as a collection of Gaussians with position and other attributes such as scale, rotation, opacity, and color. Frames capture fine details, permit views from any arbitrary perspective, but are an order of magnitude, or more, larger than 2D video frames. A line of recent work has explored how to compress dynamic 3DGS frames, but these approaches are often slow, in part because their compression techniques are not amenable to efficient acceleration. GS-NFS accelerates dynamic 3DGS compression and decompression on a GPU, to the point where it can encode and decode at full frame rate. It achieves this by developing novel GPU-based parallelizations of existing algorithms for encoding both positions and attributes of Gaussians. As a result, it is 1-2 orders of magnitude faster than the state-of-the-art in encoding and decoding a frame, while offering competitive compression performance and rendering quality.

cs.MM↗

arXiv2021

AeroTraj: Trajectory Planning for Fast, and Accurate 3D Reconstruction Using a Drone-based LiDAR

This paper presents AeroTraj, a system that enables fast, accurate, and automated reconstruction of 3D models of large buildings using a drone-mounted LiDAR. LiDAR point clouds can be used directly to assemble 3D models if their positions are accurately determined. AeroTraj uses SLAM for this, but must ensure complete and accurate reconstruction while minimizing drone battery usage. Doing this requires balancing competing constraints: drone speed, height, and orientation. AeroTraj exploits building geometry in designing an optimal trajectory that incorporates these constraints. Even with an optimal trajectory, SLAM's position error can drift over time, so AeroTraj tracks drift in-flight by offloading computations to the cloud and invokes a re-calibration procedure to minimize error. AeroTraj can reconstruct large structures with centimeter-level accuracy and with an average end-to-end latency below 250 ms, significantly outperforming the state of the art.

cs.RO↗

arXiv2018

Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources

The growing deployment of sensors as part of Internet of Things (IoT) is generating thousands of event streams. Complex Event Processing (CEP) queries offer a useful paradigm for rapid decision-making over such data sources. While often centralized in the Cloud, the deployment of capable edge devices on the field motivates the need for cooperative event analytics that span Edge and Cloud computing. Here, we identify a novel problem of query placement on edge and Cloud resources for dynamically arriving and departing analytic dataflows. We define this as an optimization problem to minimize the total makespan for all event analytics, while meeting energy and compute constraints of the resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for such dynamic dataflows, and validate them using detailed simulations for 100 - 1000 edge devices and VMs. The results show that our heuristics offer O(seconds) planning time, give a valid and high quality solution in all cases, and reduce the number of query migrations. Furthermore, rebalance strategies when applied in these heuristics have significantly reduced the makespan by around 20 - 25%.

cs.DC↗

arXiv2017

Distributed Scheduling of Event Analytics across Edge and Cloud

Internet of Things (IoT) domains generate large volumes of high velocity event streams from sensors, which need to be analyzed with low latency to drive decisions. Complex Event Processing (CEP) is a Big Data technique to enable such analytics, and is traditionally performed on Cloud Virtual Machines (VM). Leveraging captive IoT edge resources in combination with Cloud VMs can offer better performance, flexibility and monetary costs for CEP. Here, we formulate an optimization problem for energy-aware placement of CEP queries, composed as an analytics dataflow, across a collection of edge and Cloud resources, with the goal of minimizing the end-to-end latency for the dataflow. We propose a Genetic Algorithm (GA) meta-heuristic to solve this problem, and compare it against a brute-force optimal algorithm (BF). We perform detailed real-world benchmarks on the compute, network and energy capacity of edge and Cloud resources. These results are used to define a realistic and comprehensive simulation study that validates the BF and GA solutions for 45 diverse CEP dataflows, LAN and WAN setup, and different edge resource availability. We compare the GA and BF solutions against random and Cloud-only baselines for different configurations, for a total of 1764 simulation runs. Our study shows that GA is within 97% of the optimal BF solution that takes hours, maps dataflows with 4 - 50 queries in 1 - 26 secs, and only fails to offer a feasible solution <= 20% of the time.

cs.DC↗