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Despite significant improvements on the quality of results, recent deep learning approaches are computationally expensive and power-hungry, making them difficult to deploy on resource-constrained edge devices. To tackle this problem, in this article, we propose a fast, accurate, and robust registration for low-cost embedded FPGAs. Based on a parallel and pipelined PointNet feature extractor, we develop custom accelerator cores namely PointLKCore and ReAgentCore, for two different learning-based methods. They are both correspondence-free and computationally efficient as they avoid the costly feature matching step involving nearest-neighbor search. The proposed cores are implemented on the Xilinx ZCU104 board and evaluated using both synthetic and real-world datasets, showing substantial improvements in the tradeoff between runtime and registration quality. They run 58.27\u201363.21x faster than ARM Cortex-A53 CPU and offer 1.54\u201310.91x speedups over Intel Xeon CPU and Nvidia Jetson boards, while consuming less than 1 W and achieving 133.63\u2013267.57x energy efficiency compared to Nvidia GeForce GPU. The proposed cores are more robust to noise and large initial misalignments than the classical methods and quickly find reasonable solutions in less than 4\u201312 ms, demonstrating real-time performance.<\/jats:p>","DOI":"10.1145\/3717836","type":"journal-article","created":{"date-parts":[[2025,2,14]],"date-time":"2025-02-14T15:19:16Z","timestamp":1739546356000},"page":"1-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["FPGA-accelerated Correspondence-free Point Cloud Registration with PointNet Features"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8534-2381","authenticated-orcid":false,"given":"Keisuke","family":"Sugiura","sequence":"first","affiliation":[{"name":"Institute of Systems and Information Engineering, University of Tsukuba, Tsukuba, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9578-3842","authenticated-orcid":false,"given":"Hiroki","family":"Matsutani","sequence":"additional","affiliation":[{"name":"Department of Information and Computer Science, Keio University, Yokohama, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"7156","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Aoki Yasuhiro","year":"2019","unstructured":"Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, and Simon Lucey. 2019. 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