Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

Author:

Brazil Garrick1,Kumar Abhinav2,Straub Julian1,Ravi Nikhila1,Johnson Justin1,Gkioxari Georgia3

Affiliation:

1. Meta AI

2. Michigan State University

3. Caltech

Publisher

IEEE

Cited by 17 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. RoadSense3D: A Framework for Roadside Monocular 3D Object Detection;Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization;2024-06-27

2. Enhancing Inland Water Safety: The Lake Constance Obstacle Detection Benchmark;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. AEAM3D:Adverse Environment-Adaptive Monocular 3D Object Detection via Feature Extraction Regularization;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

4. Investigating the Effectiveness of 3D Monocular Object Detection Methods for Roadside Scenarios;Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing;2024-04-08

5. Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture;2024 International Conference on 3D Vision (3DV);2024-03-18

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