D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features (2003.03164v1)

Abstract: A successful point cloud registration often lies on robust establishment of sparse matches through discriminative 3D local features. Despite the fast evolution of learning-based 3D feature descriptors, little attention has been drawn to the learning of 3D feature detectors, even less for a joint learning of the two tasks. In this paper, we leverage a 3D fully convolutional network for 3D point clouds, and propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point. In particular, we propose a keypoint selection strategy that overcomes the inherent density variations of 3D point clouds, and further propose a self-supervised detector loss guided by the on-the-fly feature matching results during training. Finally, our method achieves state-of-the-art results in both indoor and outdoor scenarios, evaluated on 3DMatch and KITTI datasets, and shows its strong generalization ability on the ETH dataset. Towards practical use, we show that by adopting a reliable feature detector, sampling a smaller number of features is sufficient to achieve accurate and fast point cloud alignment.code release

• The paper introduces a unified framework that jointly learns dense 3D keypoint detection and description to enhance feature matching performance.
• It leverages deep learning to overcome traditional limitations, demonstrating superior accuracy on benchmark datasets.
• The method shows promising applications in robust 3D reconstruction and scene understanding in complex real-world environments.

Author Guidelines for CVPR Proceedings

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Technical Content

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Miscellaneous Guidance

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In conclusion, this paper serves as an essential resource for authors intending to submit to CVPR, providing detailed instructions that enhance the clarity, consistency, and professionalism of their submissions. Understanding and adhering to these guidelines is crucial for authors to ensure successful participation in the CVPR submission and review process. Future iterations may further refine these guidelines as the conference evolves and as digital and publication standards advance.