5,127 research outputs found
Monocular 3D Object Detection via Ego View-to-Bird’s Eye View Translation
The advanced development in autonomous agents like self-driving cars can be attributed to computer vision, a branch of artificial intelligence that enables software to understand the content of image and video. These autonomous agents require a three-dimensional modelling of its surrounding in order to operate reliably in the real-world. Despite the significant progress of 2D object detectors, they have a critical limitation in location sensitive applications as they do not provide accurate physical information of objects in 3D space. 3D object detection is a promising topic that can provide relevant solutions which could improve existing 2D based applications. Due to the advancements in deep learning methods and relevant datasets, the task of 3D scene understanding has evolved greatly in the past few years. 3D object detection and localization are crucial in autonomous driving tasks such as obstacle avoidance, path planning and motion control. Traditionally, there have been successful methods towards 3D object detection but they rely on highly expensive 3D LiDAR sensors for accurate depth information. On the other hand, 3D object detection from single monocular images is inexpensive but lacks in accuracy. The primary reason for such a disparity in performance is that the monocular image-based methods attempt at inferring 3D information from 2D images. In this work, we try to bridge the performance gap observed in single image input by introducing different mapping strategies between the 2D image data and its corresponding 3D representation and use it to perform object detection in 3D. The performance of the proposed method is evaluated on the popular KITTI 3D object detection benchmark dataset
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
Estimating 3D orientation and translation of objects is essential for
infrastructure-less autonomous navigation and driving. In case of monocular
vision, successful methods have been mainly based on two ingredients: (i) a
network generating 2D region proposals, (ii) a R-CNN structure predicting 3D
object pose by utilizing the acquired regions of interest. We argue that the 2D
detection network is redundant and introduces non-negligible noise for 3D
detection. Hence, we propose a novel 3D object detection method, named SMOKE,
in this paper that predicts a 3D bounding box for each detected object by
combining a single keypoint estimate with regressed 3D variables. As a second
contribution, we propose a multi-step disentangling approach for constructing
the 3D bounding box, which significantly improves both training convergence and
detection accuracy. In contrast to previous 3D detection techniques, our method
does not require complicated pre/post-processing, extra data, and a refinement
stage. Despite of its structural simplicity, our proposed SMOKE network
outperforms all existing monocular 3D detection methods on the KITTI dataset,
giving the best state-of-the-art result on both 3D object detection and Bird's
eye view evaluation. The code will be made publicly available.Comment: 8 pages, 6 figure
3D Object Detection for Autonomous Driving: A Survey
Autonomous driving is regarded as one of the most promising remedies to
shield human beings from severe crashes. To this end, 3D object detection
serves as the core basis of such perception system especially for the sake of
path planning, motion prediction, collision avoidance, etc. Generally, stereo
or monocular images with corresponding 3D point clouds are already standard
layout for 3D object detection, out of which point clouds are increasingly
prevalent with accurate depth information being provided. Despite existing
efforts, 3D object detection on point clouds is still in its infancy due to
high sparseness and irregularity of point clouds by nature, misalignment view
between camera view and LiDAR bird's eye of view for modality synergies,
occlusions and scale variations at long distances, etc. Recently, profound
progress has been made in 3D object detection, with a large body of literature
being investigated to address this vision task. As such, we present a
comprehensive review of the latest progress in this field covering all the main
topics including sensors, fundamentals, and the recent state-of-the-art
detection methods with their pros and cons. Furthermore, we introduce metrics
and provide quantitative comparisons on popular public datasets. The avenues
for future work are going to be judiciously identified after an in-deep
analysis of the surveyed works. Finally, we conclude this paper.Comment: 3D object detection, Autonomous driving, Point cloud
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