OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection

Su, Yongzhi; Di, Yan; Zhai, Guangyao; Manhardt, Fabian; Rambach, Jason; Busam, Benjamin; Stricker, Didier; Tombari, Federico

doi:10.1109/lra.2023.3238137

Cited by 16 publications

(3 citation statements)

References 43 publications

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“…The KITTI dataset contemplates occlusion for mAP calculation by considering categories of Easy, Moderate, and Hard. As another possible solution to this issue, the authors of ( Liu et al, 2022 ; Su et al, 2023 ) have exploited the anti-occlusion loss function which fuses depth and semantic information and defines a confidence occlusion parameter inside the loss. However, further investigation and analytical analysis must be carried out to effectively tackle the issue of occlusion.…”

Section: Trends In Reliable Three-dimentional Object Detectionmentioning

confidence: 99%

A survey on 3D object detection in real time for autonomous driving

Contreras,

Jain,

Bhatt

et al. 2024

Front. Robot. AI

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This survey reviews advances in 3D object detection approaches for autonomous driving. A brief introduction to 2D object detection is first discussed and drawbacks of the existing methodologies are identified for highly dynamic environments. Subsequently, this paper reviews the state-of-the-art 3D object detection techniques that utilizes monocular and stereo vision for reliable detection in urban settings. Based on depth inference basis, learning schemes, and internal representation, this work presents a method taxonomy of three classes: model-based and geometrically constrained approaches, end-to-end learning methodologies, and hybrid methods. There is highlighted segment for current trend of multi-view detectors as end-to-end methods due to their boosted robustness. Detectors from the last two kinds were specially selected to exploit the autonomous driving context in terms of geometry, scene content and instances distribution. To prove the effectiveness of each method, 3D object detection datasets for autonomous vehicles are described with their unique features, e. g., varying weather conditions, multi-modality, multi camera perspective and their respective metrics associated to different difficulty categories. In addition, we included multi-modal visual datasets, i. e., V2X that may tackle the problems of single-view occlusion. Finally, the current research trends in object detection are summarized, followed by a discussion on possible scope for future research in this domain.

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Section: Trends In Reliable Three-dimentional Object Detectionmentioning

confidence: 99%

A survey on 3D object detection in real time for autonomous driving

Contreras,

Jain,

Bhatt

et al. 2024

Front. Robot. AI

View full text Add to dashboard Cite

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“…Similarly, [23] proposes an iterative self-training framework for robotic bin-picking. [24] learns task-relevant grasping for industrial objects based on category-level pose estimation [25]- [29]. On the other hand, [30] simplifies 6D pose into 3D pose, i.e., the 2D object center and the orientation angle, and implements robotic assembly in a plane.…”

Section: Related Work a 6d Object Pose Estimationmentioning

confidence: 99%

6D Robotic Assembly Based on RGB-only Object Pose Estimation

Kun

Lian

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Comprehending natural language instructions is a critical skill for robots to cooperate effectively with humans. In this paper, we aim to learn 6D poses for robotic assembly by natural language instructions. For this purpose, Language-Instructed 6D Pose Regression Network (LanPose) is proposed to jointly predict the 6D poses of the observed object and the corresponding assembly position. Our proposed approach is based on the fusion of geometric and linguistic features, which allows us to finely integrate multi-modality input and map it to the 6D pose in SE(3) space by the cross-attention mechanism and the language-integrated 6D pose mapping module, respectively. To validate the effectiveness of our approach, an integrated robotic system is established to precisely and robustly perceive, grasp, manipulate and assemble blocks by language commands. 98.09 and 93.55 in ADD(-S)-0.1d are derived for the prediction of 6D object pose and 6D assembly pose, respectively. Both quantitative and qualitative results demonstrate the effectiveness of our proposed languageinstructed 6D pose estimation methodology and its potential to enable robots to better understand and execute natural language instructions.

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“…Trajectory prediction refers to predicting the future trajectories of one or several target agents based on past trajectories and road conditions. It is an essential and emerging subtask in autonomous driving [23,28,37,45] and industrial robotics [21,35,48].…”

Section: Introductionmentioning

confidence: 99%

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

Zhu¹,

Zhai²,

Yan³

et al. 2023

Preprint

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Reliable multi-agent trajectory prediction is crucial for the safe planning and control of autonomous systems. Compared with single-agent cases, the major challenge in simultaneously processing multiple agents lies in modeling complex social interactions caused by various driving intentions and road conditions. Previous methods typically leverage graph-based message propagation or attention mechanism to encapsulate such interactions in the format of marginal probabilistic distributions. However, it is inherently sub-optimal. In this paper, we propose IPCC-TP, a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. IPCC-TP learns pairwise joint Gaussian Distributions through the tightly-coupled estimation of the means and covariances according to interactive incremental movements. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders. Extensive experiments on nuScenes and Argoverse 2 datasets demonstrate that IPCC-TP improves the performance of baselines by a large margin.

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OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection

Cited by 16 publications

References 43 publications

A survey on 3D object detection in real time for autonomous driving

A survey on 3D object detection in real time for autonomous driving

6D Robotic Assembly Based on RGB-only Object Pose Estimation

IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction

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