LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR

Bartoccioni, Florent; Zablocki, Éloi; Pérez, Patrick; Cord, Matthieu; Alahari, Karteek

doi:10.1016/j.cviu.2022.103601

Cited by 13 publications

(5 citation statements)

References 6 publications

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“…The acquisition of accurate scale information directly from monocular images poses a common challenge for researchers, often necessitating the use of lidar [ 20 , 21 , 22 ] or stereo cameras [ 23 , 24 , 25 , 26 , 27 ] for depth estimation. Among stereo methodologies, Mo [ 24 ] proposed a fusion method that combines the advantages of tightly coupled depth sensors and stereo cameras to complete dense depth estimation, thereby achieving better depth estimation.…”

Section: Methodsmentioning

confidence: 99%

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

Tang,

Zhao,

Qiu

2024

Sensors

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This study addresses the ongoing challenge for learning-based methods to achieve accurate object detection in foggy conditions. In response to the scarcity of foggy traffic image datasets, we propose a foggy weather simulation algorithm based on monocular depth estimation. The algorithm involves a multi-step process: a self-supervised monocular depth estimation network generates a relative depth map and then applies dense geometric constraints for scale recovery to derive an absolute depth map. Subsequently, the visibility of the simulated image is defined to generate a transmittance map. The dark channel map is then used to distinguish sky regions and estimate atmospheric light values. Finally, the atmospheric scattering model is used to generate fog simulation images under specified visibility conditions. Experimental results show that more than 90% of fog images have AuthESI values of less than 2, which indicates that their non-structural similarity (NSS) characteristics are very close to those of natural fog. The proposed fog simulation method is able to convert clear images in natural environments, providing a solution to the problem of lack of foggy image datasets and incomplete visibility data.

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Section: Methodsmentioning

confidence: 99%

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

Tang,

Zhao,

Qiu

2024

Sensors

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“…In monocular setups for estimating depth, in autonomous systems, based on the vision concept, the depth of the dense type can be attained using either auxiliary input from one or more expensive LiDARs, of 64 beams, or only cameras, of which the method suffers from ambiguity in terms of scaling and infinite-depth type problems. Considering the above fact, in "LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR" [121], an innovative method of dense estimation of metric depth has been proposed which fuses the method of a monocular camera with that of LiDAR of the light-weight type. Basically, four previous architectures are refurbished and the performance difference between the self-supervised single-image depth prediction and the fully supervised depth completion on KITTI is greatly reduced.…”

Section: Self-supervised Monocular Modelsmentioning

confidence: 99%

Deep Learning-Based Stereopsis and Monocular Depth Estimation Techniques: A Review

Lahiri,

Ren,

Lin

2024

Vehicles

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A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, despite its few demerits. Sufficient progress in accuracy and depth computation speed has manifested during the period. Over the years, stereo depth estimation has been provided with various training modes, such as supervised, self-supervised, and unsupervised, before deploying it for real-time performance. These modes are to be used depending on the application and/or the availability of datasets for training. Deep learning, on the other hand, has provided the stereo depth estimation methods with a new life to breathe in the form of enhanced accuracy and quality of images, attempting to successfully reduce the residual errors in stages in some of the methods. Furthermore, depth estimation from a single RGB image has been intricate since it is an ill-posed problem with a lack of geometric constraints and ambiguities. However, this monocular depth estimation has gained popularity in recent years due to the development in the field, with appreciable improvements in the accuracy of depth maps and optimization of computational time. The help is mostly due to the usage of CNNs (Convolutional Neural Networks) and other deep learning methods, which help augment the feature-extraction phenomenon for the process and enhance the quality of depth maps/accuracy of MDE (monocular depth estimation). Monocular depth estimation has seen improvements in many algorithms that can be deployed to give depth maps with better clarity and details around the edges and fine boundaries, which thus helps in delineating between thin structures. This paper reviews various recent deep learning-based stereo and monocular depth prediction techniques emphasizing the successes achieved so far, the challenges acquainted with them, and those that can be expected shortly.

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“…Monocular cameras typically obtain depth information of the captured object through motion using structure from motion [16]. Bartoccioni et al [17] proposed a monocular camera and simple LiDAR fusion method. The LiDAR is inexpensive and only contains four scan lines.…”

Section: Monocular Depth Estimationmentioning

confidence: 99%

Robust and accurate depth estimation by fusing LiDAR and stereo

Xu,

Cao,

Liu

et al. 2023

Meas. Sci. Technol.

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Depth estimation is one of the key technologies in some fields such as autonomous driving and robot navigation.However, the traditional method of using a single sensor is inevitably limited by the sensor's performance. Therefore, a precise and robust method for fusing LiDAR and stereo cameras is proposed. This method fully combines the advantages of the LiDAR and stereo cameras, which can retain the advantages of the high precision of the LiDAR and the high resolution of images respectively. Compared with the traditional stereo matching method, the texture of the object and lighting conditions have less influence on the algorithm. Firstly, the depth of the LiDAR data is converted to the disparity of the stereo camera. Because the density of the LiDAR data is relatively sparse on the y-axis, the converted disparity map is up-sampled using the interpolation method.Secondly, in order to make full use of the precise disparity map, the disparity map and stereo-matching are fused to propagate the accurate disparity. Finally, the disparity map is converted to the depth map.Moreover, the converted disparity map can also increase the speed of the algorithm.We evaluate the proposed pipeline on the KITTI benchmark. The experiment demonstrates that our algorithm has higher accuracy than several classic methods.

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LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR

Cited by 13 publications

References 6 publications

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

A Foggy Weather Simulation Algorithm for Traffic Image Synthesis Based on Monocular Depth Estimation

Deep Learning-Based Stereopsis and Monocular Depth Estimation Techniques: A Review

Robust and accurate depth estimation by fusing LiDAR and stereo

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