Lidar Upsampling With Sliced Wasserstein Distance

Savkin, Artem; Wang, Yida; Wirkert, Sebastian J.; Navab, Nassir; Tombari, Federico

doi:10.1109/lra.2022.3214791

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…The correctness of a LiDAR PC is usually evaluated by calculating the minimum Euclidean distance between equivalent points in both a reference and the captured PC. Four metrics are typically used: the Hausdorff Distance [13], Modified Hausdorff Distance [14], Chamfer Distance, and Earth Mover's Distance [15]. The LiDAR's accuracy is then provided by the Root Mean Square Error of the calculated distances.…”

Section: Overview Of Lidar Data Testing Methodsmentioning

confidence: 99%

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

Reis,

Machado da Silva,

Correia

2023

Remote Sensing

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The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm’s confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

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Section: Overview Of Lidar Data Testing Methodsmentioning

confidence: 99%

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

Reis,

Machado da Silva,

Correia

2023

Remote Sensing

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“…First, without relying on external sources (e.g., highresolution RGB images), edges and other finely textured structures on the generated depth images are often missing, blurred or distorted [11], [12]. In [11], global and local depth variations are separated based on the fact that in the wavelet representation of the images, the fine structures mainly appear in the high-frequency domain while the global regions are defined by the low-frequency coefficients.…”

Section: Related Workmentioning

confidence: 99%

“…In order to exploit this phenomenon, they introduce a frequency-based recurrent depth coefficient refinement scheme. The difficulty of data upsampling near the edges also appears in [12], where feature extraction by an edge convolution layer is used to strengthen the precision at fine 3D structures. In our approach, we recover the fine structures by adding an appropriate edge-loss term [13] to our loss function, instead of performing edge enhancement by a dedicated sub-network.…”

Section: Related Workmentioning

confidence: 99%

“…For dynamic environment perception and recognition tasks such as advanced scene analysis and understanding, repeti-tive, typically rotating multi-beam (RMB) Lidar sensors (e.g., Ouster OS1 or Velodyne Puck models) [1] are commonly utilized devices. RMB Lidars can produce real-time point cloud streams (300k-2M points/s), however, their measurements have low spatial density, and their field of view (FoV) coverage is constant through the whole scanning process: Their vertical resolution is fixed by the number of the laser beams , while their horizontal resolution depends on the sensor's rotation frequency (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

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ST-DepthNet: A Spatio-Temporal Deep Network for Depth Completion Using a Single Non-Repetitive Circular Scanning Lidar

Zováthi

Palffy

Jankó

et al. 2023

IEEE Robot. Autom. Lett.

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In this paper, we propose a novel depth image completion technique based on sparse consecutive measurements of a non-repetitive circular scanning (NRCS) Lidar, demonstrating the capabilities of a new, compact, and accessible sensor technology for dense range mapping of highly dynamic scenes. Our deep network called ST-DepthNet is composed of a spatio-temporally (ST) extended U-Net architecture, which accepts a very sparse range data sequence as input and produces a dense depth image stream of the same field-of-view ensuring a high level of spatial details and accuracy. For evaluation, we have constructed a new urban dataset, that -to our best knowledge as the first open Benchmark in this field -comprises various simulated and realworld NRCS Lidar data samples, allowing us to simultaneously train our model on synthetic data with Ground Truth, and to validate the result via real NRCS Lidar measurements. Using this new dataset, we have shown the superiority of our method against a densified depth map obtained from the raw sensor stream, and against two independent state-of-the-art deep-learning based Lidar-only depth completion methods.

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“…where x and y are, respectively, points of P and Q. • Earth Mover's Distance (EMD): It is also known as the Discrete Wasserstein distance [39]. It is a technique for determining the degree to which two multi-dimensional distributions differ in a feature space, where a ground distance is the measurement of the distance between individual features.…”

Section: Metrics For Lidar Point Cloudmentioning

confidence: 99%

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

Karri,

da Silva,

Correia

2023

IEEE Access

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Perception algorithms are essential for autonomous or semi-autonomous vehicles to perceive the semantics of their surroundings, including object detection, panoptic segmentation, and tracking. Decision-making in case of safety-critical situations, like autonomous emergency braking and collision avoidance, relies on the outputs of these algorithms. This makes it essential to correctly assess such perception systems before their deployment and to monitor their performance when in use. It is difficult to test and validate these systems, particularly at runtime, due to the high-level and complex representations of their outputs. This paper presents an overview of different existing metrics used for the evaluation of LiDAR-based perception systems, emphasizing particularly object detection and tracking algorithms due to their importance in the final perception outcome. Along with generally used metrics, we also discuss the impact of Planning KL-Divergence (PKL), Timed Quality Temporal Logic (TQTL), and Spatio-temporal Quality Logic (STQL) metrics on object detection algorithms. In the case of panoptic segmentation, Panoptic Quality (PQ) and Parsing Covering (PC) metrics are analysed resorting to some pretrained models. Finally, it addresses the application of diverse metrics to evaluate different pretrained models with the respective perception algorithms on publicly available datasets. Besides the identification of the various metrics being proposed, their performance and influence on models are also assessed after conducting new tests or reproducing the experimental results of the reference under consideration.

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Lidar Upsampling With Sliced Wasserstein Distance

Cited by 8 publications

References 33 publications

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

ST-DepthNet: A Spatio-Temporal Deep Network for Depth Completion Using a Single Non-Repetitive Circular Scanning Lidar

Key Indicators to Assess the Performance of LiDAR-Based Perception Algorithms: A Literature Review

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