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CornerPoint3D: look at the nearest corner instead of the center

CornerPoint3D: look at the nearest corner instead of the center
CornerPoint3D: look at the nearest corner instead of the center
3D object detection aims to predict object centers, dimensions, and rotations from LiDAR point clouds. Despite its simplicity, LiDAR captures only the near side of objects, making center-based detectors prone to poor localization accuracy in cross-domain tasks with varying point distributions. Meanwhile, existing evaluation metrics designed for single-domain assessment also suffer from overfitting due to dataset-specific size variations. A key question arises: Do we really need models to maintain excellent performance in the entire 3D bounding boxes after being applied across domains? Actually, one of our main focuses is on preventing collisions between vehicles and other obstacles, especially in cross-domain scenarios where correctly predicting the sizes is much more difficult. To address these issues, we rethink cross-domain 3D object detection from a practical perspective. We propose two new metrics that evaluate a model's ability to detect objects' closer-surfaces to the LiDAR sensor. Additionally, we introduce EdgeHead, a refinement head that guides models to focus more on learnable closer surfaces, significantly improving cross-domain performance under both our new and traditional BEV/3D metrics. Furthermore, we argue that predicting the nearest corner rather than the object center enhances robustness. We propose a novel 3D object detector, coined as CornerPoint3D, which is built upon CenterPoint and uses heatmaps to supervise the learning and detection of the nearest corner of each object. Our proposed methods realize a balanced trade-off between the detection quality of entire bounding boxes and the locating accuracy of closer surfaces to the LiDAR sensor, outperforming the traditional center-based detector CenterPoint in multiple cross-domain tasks and providing a more practically reasonable and robust cross-domain 3D object detection solution.
cs.CV, cs.AI
arXiv
Zhang, Ruixiao
fc3c4eb9-b692-4ab3-8056-030cb6731fc5
Guan, Runwei
c9bbd12d-493e-4e99-a2eb-7b6150a0bde8
Chen, Xiangyu
ad3807ce-7bd5-43fb-9cdd-43674b20393f
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Zhang, Ruixiao
fc3c4eb9-b692-4ab3-8056-030cb6731fc5
Guan, Runwei
c9bbd12d-493e-4e99-a2eb-7b6150a0bde8
Chen, Xiangyu
ad3807ce-7bd5-43fb-9cdd-43674b20393f
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

3D object detection aims to predict object centers, dimensions, and rotations from LiDAR point clouds. Despite its simplicity, LiDAR captures only the near side of objects, making center-based detectors prone to poor localization accuracy in cross-domain tasks with varying point distributions. Meanwhile, existing evaluation metrics designed for single-domain assessment also suffer from overfitting due to dataset-specific size variations. A key question arises: Do we really need models to maintain excellent performance in the entire 3D bounding boxes after being applied across domains? Actually, one of our main focuses is on preventing collisions between vehicles and other obstacles, especially in cross-domain scenarios where correctly predicting the sizes is much more difficult. To address these issues, we rethink cross-domain 3D object detection from a practical perspective. We propose two new metrics that evaluate a model's ability to detect objects' closer-surfaces to the LiDAR sensor. Additionally, we introduce EdgeHead, a refinement head that guides models to focus more on learnable closer surfaces, significantly improving cross-domain performance under both our new and traditional BEV/3D metrics. Furthermore, we argue that predicting the nearest corner rather than the object center enhances robustness. We propose a novel 3D object detector, coined as CornerPoint3D, which is built upon CenterPoint and uses heatmaps to supervise the learning and detection of the nearest corner of each object. Our proposed methods realize a balanced trade-off between the detection quality of entire bounding boxes and the locating accuracy of closer surfaces to the LiDAR sensor, outperforming the traditional center-based detector CenterPoint in multiple cross-domain tasks and providing a more practically reasonable and robust cross-domain 3D object detection solution.

Text
2504.02464v1 - Author's Original
Available under License Creative Commons Attribution.
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More information

Published date: 3 April 2025
Additional Information: arXiv admin note: substantial text overlap with arXiv:2407.04061
Keywords: cs.CV, cs.AI

Identifiers

Local EPrints ID: 501769
URI: http://eprints.soton.ac.uk/id/eprint/501769
PURE UUID: 12ef9d33-1a49-444c-be1e-67b19e6722b7
ORCID for Xiaohao Cai: ORCID iD orcid.org/0000-0003-0924-2834

Catalogue record

Date deposited: 09 Jun 2025 18:06
Last modified: 10 Jun 2025 02:04

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Contributors

Author: Ruixiao Zhang
Author: Runwei Guan
Author: Xiangyu Chen
Author: Adam Prugel-Bennett
Author: Xiaohao Cai ORCID iD

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