Graphite: A GPU-Accelerated Mixed-Precision Graph Optimization Framework
We present Graphite, a GPU-accelerated nonlinear least squares graph optimization framework. It provides a CUDA C++ interface to enable the sharing of code between a real-time application, such as a SLAM system, and its optimization tasks. The framework supports techniques to reduce memory usage, including in-place optimization, support for multiple floating point types and mixed-precision modes, and dynamically computed Jacobians. We evaluate Graphite on well-known bundle adjustment problems and find that it achieves similar performance to MegBA, a solver specialized for bundle adjustment, while maintaining generality and using less memory. We also apply Graphite to global visual-inertial bundle adjustment on maps generated from stereo-inertial SLAM datasets, and observe speed-ups of up to 59x compared to a CPU baseline. Our results indicate that our framework enables faster large-scale optimization on both desktop and resource-constrained devices.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
GRAPHITE: Graph-Based Interpretable Tissue Examination for Enhanced Explainability in Breast Cancer Histopathology
Explainable AI (XAI) in medical histopathology is essential for enhancing the interpretability and clinical trustworthiness of deep learning models in cancer diagnosis. However, the black-box nature of these models often…
DiagnosticGraph Attentionwhole slide imagesGRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems
This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the real-world: 1) automatically constraining…
BIG-bench Machine LearningGeneral ClassificationLicense Plate RecognitionSecuring the supply of graphite for batteries
The increasing demand for graphite in batteries has led to concerns around supply chain security. Currently, over 92% of global anode material is produced in China, posing a geopolitical risk for other countries reliant …
GraphITE: Estimating Individual Effects of Graph-structured Treatments
Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations. Most existing outcome estimation methods deal with binary or multiple-choice treatments; h…
counterfactualDecision MakingMultiple-choiceGraphite: Iterative Generative Modeling of Graphs
Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computat…
Density EstimationGeneral ClassificationLink PredictionNode Classification+1