From Points to Parts : 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network | PERPUSTAKAAN UNIVERSITAS KATOLIK PARAHYANGAN

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From Points to Parts : 3D Object Detection From Point Cloud With Part-Aware and Part-Aggregation Network

Shi, Shaoshuai - Nama Orang; Li, Hongsheng - Nama Orang; Wang, Xiaogang - Nama Orang; Wang, Zhe - Nama Orang; Shi, Jianping - Nama Orang;

3D object detection from LiDAR point cloud is a challenging problem in 3D scene understanding and has many practical applications. In this paper, we extend our preliminary work PointRCNN to a novel and strong point-cloud-based 3D object detection framework, the part-aware and aggregation neural network (Part-A 2 net). The whole framework consists of the part-aware stage and the part-aggregation stage. First, the part-aware stage for the first time fully utilizes free-of-charge part supervisions derived from 3D ground-truth boxes to simultaneously predict high quality 3D proposals and accurate intra-object part locations. The predicted intra-object part locations within the same proposal are grouped by our new-designed RoI-aware point cloud pooling module, which results in an effective representation to encode the geometry-specific features of each 3D proposal. Then the part-aggregation stage learns to re-score the box and refine the box location by exploring the spatial relationship of the pooled intra-object part locations. Extensive experiments are conducted to demonstrate the performance improvements from each component of our proposed framework. Our Part-A 2 net outperforms all existing 3D detection methods and achieves new state-of-the-art on KITTI 3D object detection dataset by utilizing only the LiDAR point cloud data.

Ketersediaan

Barcode		Tipe Koleksi	Nomor Panggil	Lokasi	Status
art138524	null	Artikel		Gdg9-Lt3	Tersedia namun tidak untuk dipinjamkan - No Loan

Informasi Detail

Judul Seri: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.8 August 2021
No. Panggil: -
Penerbit: : .,
Deskripsi Fisik: p. 2647 - 2664
Bahasa: English
ISBN/ISSN: -
Klasifikasi: -
Tipe Isi: -
Tipe Media: -
Tipe Pembawa: -
Edisi: -
Subjek: LIDAR
AUTONOMOUS DRIVING
CONVOLUTIONAL NEURAL NETWORK
3D OBJECT DETECTION
POINT CLOUD
PART LOCATION
Info Detail Spesifik: DOI: 10.1109/TPAMI.2020.2977026
Pernyataan Tanggungjawab: Shaoshuai Shi, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li

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