{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/rl-gan-net-a-reinforcement-learning-agent","title":"RL-GAN-Net: A Reinforcement Learning Agent Controlled GAN Network for Real-Time Point Cloud Shape Completion","arxiv_id":"1904.12304","date":"2019-04-28","proceeding":"CVPR 2019 6","authors":["Muhammad Sarmad","Hyunjoo Jenny Lee","Young Min Kim"],"abstract":"We present RL-GAN-Net, where a reinforcement learning (RL) agent provides\nfast and robust control of a generative adversarial network (GAN). Our\nframework is applied to point cloud shape completion that converts noisy,\npartial point cloud data into a high-fidelity completed shape by controlling\nthe GAN. While a GAN is unstable and hard to train, we circumvent the problem\nby (1) training the GAN on the latent space representation whose dimension is\nreduced compared to the raw point cloud input and (2) using an RL agent to find\nthe correct input to the GAN to generate the latent space representation of the\nshape that best fits the current input of incomplete point cloud. The suggested\npipeline robustly completes point cloud with large missing regions. To the best\nof our knowledge, this is the first attempt to train an RL agent to control the\nGAN, which effectively learns the highly nonlinear mapping from the input noise\nof the GAN to the latent space of point cloud. The RL agent replaces the need\nfor complex optimization and consequently makes our technique real time.\nAdditionally, we demonstrate that our pipelines can be used to enhance the\nclassification accuracy of point cloud with missing data.","url_abs":"http://arxiv.org/abs/1904.12304v1","url_pdf":"http://arxiv.org/pdf/1904.12304v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"rl-gan-net-a-reinforcement-learning-agent","repo_url":"https://github.com/apoorvkhattar/RL-Project-2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"rl-gan-net-a-reinforcement-learning-agent","repo_url":"https://github.com/iSarmad/RL-GAN-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":null,"task_name":"Generative Adversarial Network"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"reinforcement-learning-1","task_name":"Reinforcement Learning (RL)"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12304"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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