OdoViz: A 3D Odometry Visualization and Processing Tool
- URL: http://arxiv.org/abs/2107.07557v1
- Date: Thu, 15 Jul 2021 18:37:19 GMT
- Title: OdoViz: A 3D Odometry Visualization and Processing Tool
- Authors: Saravanabalagi Ramachandran and John McDonald
- Abstract summary: OdoViz is a reactive web-based tool for 3D visualization and processing of autonomous vehicle datasets.
The system includes functionality for loading, inspecting, visualizing, and processing GPS/INS poses, point clouds and camera images.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: OdoViz is a reactive web-based tool for 3D visualization and processing of
autonomous vehicle datasets designed to support common tasks in visual place
recognition research. The system includes functionality for loading,
inspecting, visualizing, and processing GPS/INS poses, point clouds and camera
images. It supports a number of commonly used driving datasets and can be
adapted to load custom datasets with minimal effort. OdoViz's design consists
of a slim server to serve the datasets coupled with a rich client frontend.
This design supports multiple deployment configurations including single user
stand-alone installations, research group installations serving datasets
internally across a lab, or publicly accessible web-frontends for providing
online interfaces for exploring and interacting with datasets. The tool allows
viewing complete vehicle trajectories traversed at multiple different time
periods simultaneously, facilitating tasks such as sub-sampling, comparing and
finding pose correspondences both across and within sequences. This
significantly reduces the effort required in creating subsets of data from
existing datasets for machine learning tasks. Further to the above, the system
also supports adding custom extensions and plugins to extend the capabilities
of the software for other potential data management, visualization and
processing tasks. The platform has been open-sourced to promote its use and
encourage further contributions from the research community.
Related papers
- AEye: A Visualization Tool for Image Datasets [18.95453617434051]
AEye is a semantically meaningful visualization tool tailored to image datasets.
AEye embeds images into semantically meaningful high-dimensional representations, facilitating data clustering and organization.
AEye facilitates semantic search functionalities for both text and image queries, enabling users to search for content.
arXiv Detail & Related papers (2024-08-07T20:19:20Z) - M3SOT: Multi-frame, Multi-field, Multi-space 3D Single Object Tracking [41.716532647616134]
3D Single Object Tracking (SOT) stands a forefront task of computer vision, proving essential for applications like autonomous driving.
In this research, we unveil M3SOT, a novel 3D SOT framework, which synergizes multiple input frames (template sets), multiple receptive fields (continuous contexts), and multiple solution spaces (distinct tasks) in ONE model.
arXiv Detail & Related papers (2023-12-11T04:49:47Z) - Multi-task Learning with 3D-Aware Regularization [55.97507478913053]
We propose a structured 3D-aware regularizer which interfaces multiple tasks through the projection of features extracted from an image encoder to a shared 3D feature space.
We show that the proposed method is architecture agnostic and can be plugged into various prior multi-task backbones to improve their performance.
arXiv Detail & Related papers (2023-10-02T08:49:56Z) - VisionKG: Unleashing the Power of Visual Datasets via Knowledge Graph [2.3143591448419074]
Vision Knowledge Graph (VisionKG) is a novel resource that interlinks, organizes and manages visual datasets via knowledge graphs and Semantic Web technologies.
VisionKG currently contains 519 million RDF triples that describe approximately 40 million entities.
arXiv Detail & Related papers (2023-09-24T11:19:13Z) - Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects.
We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
arXiv Detail & Related papers (2023-08-21T10:38:32Z) - Collection Space Navigator: An Interactive Visualization Interface for
Multidimensional Datasets [0.0]
Collection Space Navigator (CSN) is a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts.
CSN provides a customizable interface that combines two-dimensional projections with a set of multidimensional filters.
Users can reconfigure the interface to fit their own data and research needs, including projections and filter controls.
arXiv Detail & Related papers (2023-05-11T14:03:26Z) - Argoverse 2: Next Generation Datasets for Self-Driving Perception and
Forecasting [64.7364925689825]
Argoverse 2 (AV2) is a collection of three datasets for perception and forecasting research in the self-driving domain.
The Lidar dataset contains 20,000 sequences of unlabeled lidar point clouds and map-aligned pose.
The Motion Forecasting dataset contains 250,000 scenarios mined for interesting and challenging interactions between the autonomous vehicle and other actors in each local scene.
arXiv Detail & Related papers (2023-01-02T00:36:22Z) - ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data
Format [88.33443450434521]
Task-oriented dialogue (TOD) systems function as digital assistants, guiding users through various tasks such as booking flights or finding restaurants.
Existing toolkits for building TOD systems often fall short of in delivering comprehensive arrays of data, models, and experimental environments.
We introduce ConvLab-3: a multifaceted dialogue system toolkit crafted to bridge this gap.
arXiv Detail & Related papers (2022-11-30T16:37:42Z) - Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision
Datasets from 3D Scans [103.92680099373567]
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world.
Changing the sampling parameters allows one to "steer" the generated datasets to emphasize specific information.
Common architectures trained on a generated starter dataset reached state-of-the-art performance on multiple common vision tasks and benchmarks.
arXiv Detail & Related papers (2021-10-11T04:21:46Z) - An Extensible Dashboard Architecture For Visualizing Base And Analyzed
Data [2.169919643934826]
This paper focuses on an architecture for visualization of base as well as analyzed data.
This paper proposes a modular architecture of a dashboard for user-interaction, visualization management, and complex analysis of base data.
arXiv Detail & Related papers (2021-06-09T19:45:43Z) - Improving Point Cloud Semantic Segmentation by Learning 3D Object
Detection [102.62963605429508]
Point cloud semantic segmentation plays an essential role in autonomous driving.
Current 3D semantic segmentation networks focus on convolutional architectures that perform great for well represented classes.
We propose a novel Aware 3D Semantic Detection (DASS) framework that explicitly leverages localization features from an auxiliary 3D object detection task.
arXiv Detail & Related papers (2020-09-22T14:17:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.