A Survey on 3D Object Detection Methods for Autonomous Driving Applications

Multi-Object Tracking (MOT) is an integral part of any autonomous driving pipelines because it produces trajectories of other moving objects in the scene and predicts their future motion. Thanks to the recent advances in 3D object detection enabled by deep learning, track-by-detection has become the dominant paradigm in 3D MOT. In this paradigm, a MOT system is essentially made of an object detector and a data association algorithm which establishes track-to-detection correspondence. While 3D object detection has been actively researched, association algorithms for 3D MOT has settled at bipartite matching formulated as a Linear Assignment Problem (LAP) and solved by the Hungarian algorithm. In this paper, we adapt a two-stage data association method which was successfully applied to image-based tracking to the 3D setting, thus providing an alternative for data association for 3D MOT. Our method outperforms the baseline using one-stage bipartite matching for data association by achieving 0.587 Average Multi-Object Tracking Accuracy (AMOTA) in NuScenes validation set and 0.365 AMOTA (at level 2) in Waymo test set.

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A Survey on Deep Learning Based Methods and Datasets for Monocular 3D Object Detection

Electronics ◽

10.3390/electronics10040517 ◽

2021 ◽

Vol 10 (4) ◽

pp. 517

Author(s):

Seong-heum Kim ◽

Youngbae Hwang

Keyword(s):

Deep Learning ◽

Object Detection ◽

Low Cost ◽

Detection Methods ◽

Future Research ◽

3D Object ◽

Practical Applications ◽

Depth Sensors ◽

Significant Research ◽

3D Object Detection

Owing to recent advancements in deep learning methods and relevant databases, it is becoming increasingly easier to recognize 3D objects using only RGB images from single viewpoints. This study investigates the major breakthroughs and current progress in deep learning-based monocular 3D object detection. For relatively low-cost data acquisition systems without depth sensors or cameras at multiple viewpoints, we first consider existing databases with 2D RGB photos and their relevant attributes. Based on this simple sensor modality for practical applications, deep learning-based monocular 3D object detection methods that overcome significant research challenges are categorized and summarized. We present the key concepts and detailed descriptions of representative single-stage and multiple-stage detection solutions. In addition, we discuss the effectiveness of the detection models on their baseline benchmarks. Finally, we explore several directions for future research on monocular 3D object detection.

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Strong-Weak Feature Alignment for 3D Object Detection

Electronics ◽

10.3390/electronics10101205 ◽

2021 ◽

Vol 10 (10) ◽

pp. 1205

Author(s):

Zhiyu Wang ◽

Li Wang ◽

Bin Dai

Keyword(s):

Object Detection ◽

Point Clouds ◽

Autonomous Driving ◽

Feature Representation ◽

Alignment Algorithm ◽

3D Object ◽

3D Point Clouds ◽

Object Feature ◽

3D Object Detection ◽

Feature Alignment

Object detection in 3D point clouds is still a challenging task in autonomous driving. Due to the inherent occlusion and density changes of the point cloud, the data distribution of the same object will change dramatically. Especially, the incomplete data with sparsity or occlusion can not represent the complete characteristics of the object. In this paper, we proposed a novel strong–weak feature alignment algorithm between complete and incomplete objects for 3D object detection, which explores the correlations within the data. It is an end-to-end adaptive network that does not require additional data and can be easily applied to other object detection networks. Through a complete object feature extractor, we achieve a robust feature representation of the object. It serves as a guarding feature to help the incomplete object feature generator to generate effective features. The strong–weak feature alignment algorithm reduces the gap between different states of the same object and enhances the ability to represent the incomplete object. The proposed adaptation framework is validated on the KITTI object benchmark and gets about 6% improvement in detection average precision on 3D moderate difficulty compared to the basic model. The results show that our adaptation method improves the detection performance of incomplete 3D objects.

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Monocular 3D Object Detection for Autonomous Driving

2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr.2016.236 ◽

2016 ◽

Cited By ~ 221

Author(s):

Xiaozhi Chen ◽

Kaustav Kundu ◽

Ziyu Zhang ◽

Huimin Ma ◽

Sanja Fidler ◽

...

Keyword(s):

Object Detection ◽

Autonomous Driving ◽

3D Object ◽

3D Object Detection

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A Novel Regional Fusion Network for 3D Object Detection based on RGB Images and Point Clouds

10.5121/csit.2021.111812 ◽

2021 ◽

Author(s):

Hung-Hao Chen ◽

Chia-Hung Wang ◽

Hsueh-Wei Chen ◽

Pei-Yung Hsiao ◽

Li-Chen Fu ◽

...

Keyword(s):

Object Detection ◽

Receptive Fields ◽

Point Clouds ◽

Detection Methods ◽

Lidar Data ◽

3D Object ◽

Multi Scale ◽

Interest Level ◽

Rgb Images ◽

3D Object Detection

The current fusion-based methods transform LiDAR data into bird’s eye view (BEV) representations or 3D voxel, leading to information loss and heavy computation cost of 3D convolution. In contrast, we directly consume raw point clouds and perform fusion between two modalities. We employ the concept of region proposal network to generate proposals from two streams, respectively. In order to make two sensors compensate the weakness of each other, we utilize the calibration parameters to project proposals from one stream onto the other. With the proposed multi-scale feature aggregation module, we are able to combine the extracted regionof-interest-level (RoI-level) features of RGB stream from different receptive fields, resulting in fertilizing feature richness. Experiments on KITTI dataset show that our proposed network outperforms other fusion-based methods with meaningful improvements as compared to 3D object detection methods under challenging setting.

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R-CNN Based 3D Object Detection for Autonomous Driving

CICTP 2020 ◽

10.1061/9780784483053.077 ◽

2020 ◽

Author(s):

Hongyu Hu ◽

Tongtong Zhao ◽

Qi Wang ◽

Fei Gao ◽

Lei He

Keyword(s):

Object Detection ◽

Autonomous Driving ◽

3D Object ◽

3D Object Detection

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Stereo R-CNN Based 3D Object Detection for Autonomous Driving

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ◽

10.1109/cvpr.2019.00783 ◽

2019 ◽

Cited By ~ 48

Author(s):

Peiliang Li ◽

Xiaozhi Chen ◽

Shaojie Shen

Keyword(s):

Object Detection ◽

Autonomous Driving ◽

3D Object ◽

3D Object Detection

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ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6945 ◽

2020 ◽

Vol 34 (07) ◽

pp. 12557-12564 ◽

Cited By ~ 4

Author(s):

Zhenbo Xu ◽

Wei Zhang ◽

Xiaoqing Ye ◽

Xiao Tan ◽

Wei Yang ◽

...

Keyword(s):

Object Detection ◽

Point Clouds ◽

Autonomous Driving ◽

Disparity Estimation ◽

3D Object ◽

Detection Model ◽

Occluded Objects ◽

Bounding Boxes ◽

Detection Quality ◽

3D Object Detection

3D object detection is an essential task in autonomous driving and robotics. Though great progress has been made, challenges remain in estimating 3D pose for distant and occluded objects. In this paper, we present a novel framework named ZoomNet for stereo imagery-based 3D detection. The pipeline of ZoomNet begins with an ordinary 2D object detection model which is used to obtain pairs of left-right bounding boxes. To further exploit the abundant texture cues in rgb images for more accurate disparity estimation, we introduce a conceptually straight-forward module – adaptive zooming, which simultaneously resizes 2D instance bounding boxes to a unified resolution and adjusts the camera intrinsic parameters accordingly. In this way, we are able to estimate higher-quality disparity maps from the resized box images then construct dense point clouds for both nearby and distant objects. Moreover, we introduce to learn part locations as complementary features to improve the resistance against occlusion and put forward the 3D fitting score to better estimate the 3D detection quality. Extensive experiments on the popular KITTI 3D detection dataset indicate ZoomNet surpasses all previous state-of-the-art methods by large margins (improved by 9.4% on APbv (IoU=0.7) over pseudo-LiDAR). Ablation study also demonstrates that our adaptive zooming strategy brings an improvement of over 10% on AP3d (IoU=0.7). In addition, since the official KITTI benchmark lacks fine-grained annotations like pixel-wise part locations, we also present our KFG dataset by augmenting KITTI with detailed instance-wise annotations including pixel-wise part location, pixel-wise disparity, etc.. Both the KFG dataset and our codes will be publicly available at https://github.com/detectRecog/ZoomNet.

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