{"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/learning-to-navigate-the-energy-landscape","title":"Learning to Navigate the Energy Landscape","arxiv_id":"1603.05772","date":"2016-03-18","proceeding":null,"authors":["Julien Valentin","Angela Dai","Matthias Nießner","Pushmeet Kohli","Philip Torr","Shahram Izadi","Cem Keskin"],"abstract":"In this paper, we present a novel and efficient architecture for addressing\ncomputer vision problems that use `Analysis by Synthesis'. Analysis by\nsynthesis involves the minimization of the reconstruction error which is\ntypically a non-convex function of the latent target variables.\nState-of-the-art methods adopt a hybrid scheme where discriminatively trained\npredictors like Random Forests or Convolutional Neural Networks are used to\ninitialize local search algorithms. While these methods have been shown to\nproduce promising results, they often get stuck in local optima. Our method\ngoes beyond the conventional hybrid architecture by not only proposing multiple\naccurate initial solutions but by also defining a navigational structure over\nthe solution space that can be used for extremely efficient gradient-free local\nsearch. We demonstrate the efficacy of our approach on the challenging problem\nof RGB Camera Relocalization. To make the RGB camera relocalization problem\nparticularly challenging, we introduce a new dataset of 3D environments which\nare significantly larger than those found in other publicly-available datasets.\nOur experiments reveal that the proposed method is able to achieve\nstate-of-the-art camera relocalization results. We also demonstrate the\ngeneralizability of our approach on Hand Pose Estimation and Image Retrieval\ntasks.","url_abs":"http://arxiv.org/abs/1603.05772v1","url_pdf":"http://arxiv.org/pdf/1603.05772v1.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":[],"tasks":[{"task_slug":"camera-relocalization","task_name":"Camera Relocalization"},{"task_slug":"hand-pose-estimation","task_name":"Hand Pose Estimation"},{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[{"slug":"12-scenes","name":"12 Scenes","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1603.05772","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}