PQM: A Point Quality Evaluation Metric for Dense Maps

• URL: http://arxiv.org/abs/2306.03660v1
• Date: Tue, 6 Jun 2023 13:23:42 GMT
• Title: PQM: A Point Quality Evaluation Metric for Dense Maps
• Authors: Yash Turkar, Pranay Meshram, Charuvahan Adhivarahan, Karthik Dantu
• Abstract summary: We propose a novel point quality evaluation metric (PQM) that consists of four sub-metrics to provide a more comprehensive evaluation of point cloud quality. The completeness sub-metric evaluates the proportion of missing data, the artifact score sub-metric recognizes and characterizes artifacts, the accuracy sub-metric measures registration accuracy, and the resolution sub-metric quantifies point cloud density.
• Score: 8.749752439260199
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LiDAR-based mapping/reconstruction are important for various applications, but evaluating the quality of the dense maps they produce is challenging. The current methods have limitations, including the inability to capture completeness, structural information, and local variations in error. In this paper, we propose a novel point quality evaluation metric (PQM) that consists of four sub-metrics to provide a more comprehensive evaluation of point cloud quality. The completeness sub-metric evaluates the proportion of missing data, the artifact score sub-metric recognizes and characterizes artifacts, the accuracy sub-metric measures registration accuracy, and the resolution sub-metric quantifies point cloud density. Through an ablation study using a prototype dataset, we demonstrate the effectiveness of each of the sub-metrics and compare them to popular point cloud distance measures. Using three LiDAR SLAM systems to generate maps, we evaluate their output map quality and demonstrate the metrics robustness to noise and artifacts. Our implementation of PQM, datasets and detailed documentation on how to integrate with your custom dense mapping pipeline can be found at github.com/droneslab/pqm

Related papers

• Good Keypoints for the Two-View Geometry Estimation Problem [48.82616806880012]
  We propose a new theoretical model for scoring feature points (keypoints) in the context of the two-view geometry estimation problem. The model determines two properties that a good keypoint for solving the homography estimation problem should have: be repeatable and have a small expected measurement error. We use the developed model to design a method that detects keypoints that benefit the homography estimation introducing the Bounded NeSS-ST keypoint detector.
  arXiv  Detail & Related papers  (2025-03-24T15:15:36Z)
• Low-Complexity Patch-based No-Reference Point Cloud Quality Metric exploiting Weighted Structure and Texture Features [5.409704301731714]
  PST-PCQA is a no-reference point cloud quality metric based on a low-complexity, learning-based framework. It evaluates point cloud quality by analyzing individual patches, integrating local and global features to predict the Mean Opinion Score.
  arXiv  Detail & Related papers  (2025-03-19T08:52:04Z)
• TanDepth: Leveraging Global DEMs for Metric Monocular Depth Estimation in UAVs [5.6168844664788855]
  This work presents TanDepth, a practical, online scale recovery method for obtaining metric depth results from relative estimations at inference-time. Tailored for Unmanned Aerial Vehicle (UAV) applications, our method leverages sparse measurements from Global Digital Elevation Models (GDEM) by projecting them to the camera view. An adaptation to the Cloth Simulation Filter is presented, which allows selecting ground points from the estimated depth map to then correlate with the projected reference points.
  arXiv  Detail & Related papers  (2024-09-08T15:54:43Z)
• From Displacements to Distributions: A Machine-Learning Enabled Framework for Quantifying Uncertainties in Parameters of Computational Models [0.09208007322096533]
  This work presents novel extensions for combining two frameworks for quantifying uncertainties in the modeling of engineered systems. The data-consistent iteration (DC) framework poses an inverse problem and solution for quantifying aleatoric uncertainties in terms of pullback and push-forward measures for a given Quantity of Interest (QoI) map. The Learning Uncertain Quantities (LUQ) framework defines a formal three-step machine-learning enabled process for transforming noisy datasets into samples of a learned QoI map.
  arXiv  Detail & Related papers  (2024-03-04T20:40:50Z)
• Simple Baselines for Projection-based Full-reference and No-reference Point Cloud Quality Assessment [60.2709006613171]
  We propose simple baselines for projection-based point cloud quality assessment (PCQA) We use multi-projections obtained via a common cube-like projection process from the point clouds for both full-reference (FR) and no-reference (NR) PCQA tasks. Taking part in the ICIP 2023 PCVQA Challenge, we succeeded in achieving the top spot in four out of the five competition tracks.
  arXiv  Detail & Related papers  (2023-10-26T04:42:57Z)
• PointPatchMix: Point Cloud Mixing with Patch Scoring [58.58535918705736]
  We propose PointPatchMix, which mixes point clouds at the patch level and generates content-based targets for mixed point clouds. Our approach preserves local features at the patch level, while the patch scoring module assigns targets based on the content-based significance score from a pre-trained teacher model. With Point-MAE as our baseline, our model surpasses previous methods by a significant margin, achieving 86.3% accuracy on ScanObjectNN and 94.1% accuracy on ModelNet40.
  arXiv  Detail & Related papers  (2023-03-12T14:49:42Z)
• TCDM: Transformational Complexity Based Distortion Metric for Perceptual Point Cloud Quality Assessment [24.936061591860838]
  The goal of objective point cloud quality assessment (PCQA) research is to develop metrics that measure point cloud quality in a consistent manner. We evaluate the point cloud quality by measuring the complexity of transforming the distorted point cloud back to its reference. The effectiveness of the proposed transformational complexity based distortion metric (TCDM) is evaluated through extensive experiments conducted on five public point cloud quality assessment databases.
  arXiv  Detail & Related papers  (2022-10-10T13:20:51Z)
• Point Cloud Quality Assessment using 3D Saliency Maps [37.290843791053256]
  We propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction. Specifically, we first propose a projection-based point cloud saliency map generation method, in which depth information is introduced to better reflect the geometric characteristics of point clouds. Finally, a saliency-based pooling strategy is proposed to generate the final quality score.
  arXiv  Detail & Related papers  (2022-09-30T13:59:09Z)
• Unveiling The Mask of Position-Information Pattern Through the Mist of Image Features [75.62755703738696]
  Recent studies show that paddings in convolutional neural networks encode absolute position information. Existing metrics for quantifying the strength of positional information remain unreliable. We propose novel metrics for measuring (and visualizing) the encoded positional information.
  arXiv  Detail & Related papers  (2022-06-02T17:59:57Z)
• OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data [8.486063950768694]
  We propose a method to generate attribution maps for 3D object detection in LiDAR point clouds. These maps indicate the importance of each 3D point in predicting the specific objects. We show a detailed evaluation of the attribution maps and demonstrate that they are interpretable and highly informative.
  arXiv  Detail & Related papers  (2022-04-13T18:00:30Z)
• Towards Question-Answering as an Automatic Metric for Evaluating the Content Quality of a Summary [65.37544133256499]
  We propose a metric to evaluate the content quality of a summary using question-answering (QA) We demonstrate the experimental benefits of QA-based metrics through an analysis of our proposed metric, QAEval.
  arXiv  Detail & Related papers  (2020-10-01T15:33:09Z)
• Rethinking Localization Map: Towards Accurate Object Perception with Self-Enhancement Maps [78.2581910688094]
  This work introduces a novel self-enhancement method to harvest accurate object localization maps and object boundaries with only category labels as supervision. In particular, the proposed Self-Enhancement Maps achieve the state-of-the-art localization accuracy of 54.88% on ILSVRC.
  arXiv  Detail & Related papers  (2020-06-09T12:35:55Z)
• JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method [92.15895515035795]
  We introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD++) that contains "4,372" images with "1.51 million" annotations. We propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation.
  arXiv  Detail & Related papers  (2020-04-07T14:59:35Z)

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.