{"type":"ScholarlyArticle","title":"APR: Online Distant Point Cloud Registration through Aggregated Point Cloud Reconstruction","authors":[{"name":"Quan Liu","slug":"quan-liu","given":"Quan","family":"Liu","affiliation":"Shanghai Jiao Tong University"},{"name":"Yunsong Zhou","slug":"yunsong-zhou","given":"Yunsong","family":"Zhou","affiliation":"Shanghai Jiao Tong University"},{"name":"Hongzi Zhu","slug":"hongzi-zhu","given":"Hongzi","family":"Zhu","affiliation":"Shanghai Jiao Tong University"},{"name":"Shan Chang","slug":"shan-chang","given":"Shan","family":"Chang","affiliation":"Donghua University"},{"name":"Minyi Guo","slug":"minyi-guo","given":"Minyi","family":"Guo","affiliation":"Shanghai Jiao Tong University"}],"abstract":"For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, we propose a novel feature extraction framework, called APR, for online distant point cloud registration. Specifically, APR leverages an autoencoder design, where the autoencoder reconstructs a denser aggregated point cloud with several frames instead of the original single input point cloud. Our design forces the encoder to extract features with rich local geometry information based on one single input point cloud. Such features are then used for online distant point cloud registration. We conduct extensive experiments against state-of-the-art (SOTA) feature extractors on KITTI and nuScenes datasets. Results show that APR outperforms all other extractors by a large margin, increasing average registration recall of SOTA extractors by 7.1% on LoKITTI and 4.6% on LoNuScenes. Code is available at https://github.com/liuQuan98/APR.","doi":"10.24963/ijcai.2023/134","url":"https://ijcai.org/proceedings/2023/134","pdf":"https://ijcai.org/proceedings/2023/0134.pdf","markdown":"https://ijcai.org/proceedings/2023/134.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/134","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":134,"pages":{"first":1204,"last":1212},"keywords":["Computer Vision: CV: Machine learning for vision","Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Ali et al., 2021] Sk Aziz Ali, Kerem Kahraman, Gerd Reis, and Didier Stricker. Rpsrnet: End-to-end trainable rigid point set registration network using barnes-hut 2d-tree rep- resentation. In Proceedings of the IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition, pages 13100-13110, 2021.","title":"Rpsrnet: End-to-end trainable rigid point set registration network using barnes-hut 2d-tree representation","authors":"Ali","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ao et al., 2021] Sheng Ao, Qingyong Hu, Bo Yang, An- drew Markham, and Yulan Guo. Spinnet: Learning a general surface descriptor for 3d point cloud registra- tion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11753- 11762, 2021.","title":"Spinnet: Learning a general surface descriptor for 3d point cloud registration","authors":"Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Aoki et al., 2019] Yasuhiro Aoki, Hunter Goforth, Ran- gaprasad Arun Srivatsan, and Simon Lucey. Pointnetlk: Robust & efficient point cloud registration using pointnet. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7163-7172, 2019.","title":"Pointnetlk: Robust & efficient point cloud registration using pointnet","authors":"Yasuhiro Aoki, Hunter Goforth, Rangaprasad Arun Srivatsan, Simon Lucey","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bai et al., 2020] Xuyang Bai, Zixin Luo, Lei Zhou, Hongbo Fu, Long Quan, and Chiew-Lan Tai. D3feat: Joint learning of dense detection and description of 3d local features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6359-6367, 2020.","title":"D3feat: Joint learning of dense detection and description of 3d local features","authors":"Bai","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bai et al., 2021] Xuyang Bai, Zixin Luo, Lei Zhou, Hongkai Chen, Lei Li, Zeyu Hu, Hongbo Fu, and Chiew- Lan Tai. Pointdsc: Robust point cloud registration using deep spatial consistency. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15859-15869, 2021.","title":"Pointdsc: Robust point cloud registration using deep spatial consistency","authors":"Bai","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Caesar et al., 2020] Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2020.","title":"nuscenes: A multimodal dataset for autonomous driving","authors":"Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, Oscar Beijbom","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Choi et al., 2015] Sungjoon Choi, Qian-Yi Zhou, and Vladlen Koltun. Robust reconstruction of indoor scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 2015.","title":"Robust reconstruction of indoor scenes","authors":"Choi","year":"2015","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Choy et al., 2019] Christopher Choy, Jaesik Park, and Vladlen Koltun. Fully convolutional geometric features. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.","title":"Fully convolutional geometric features","authors":"Christopher Choy, Jaesik Park, Vladlen Koltun","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Choy et al., 2020] Christopher Choy, Wei Dong, and Vladlen Koltun. Deep global registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 2514-2523, 2020.","title":"Deep global registration","authors":"Christopher Choy, Wei Dong, Vladlen Koltun","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Deng et al., 2018] Haowen Deng, Tolga Birdal, and Slobo- dan Ilic. Ppfnet: Global context aware local features for robust 3d point matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 195-205, 2018.","title":"Ppfnet: Global context aware local features for robust 3d point matching","authors":"Deng","year":"2018","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Geiger et al., 2012] Andreas Geiger, Philip Lenz, and Raquel Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2012.","title":"Are we ready for autonomous driving? the kitti vision benchmark suite","authors":"Andreas Geiger, Philip Lenz, Raquel Urtasun","year":"2012","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Gojcic et al., 2019] Zan Gojcic, Caifa Zhou, Jan D Wegner, and Andreas Wieser. The perfect match: 3d point cloud matching with smoothed densities. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5545-5554, 2019.","title":"The perfect match: 3d point cloud matching with smoothed densities","authors":"Zan Gojcic, Caifa Zhou, Jan D Wegner, Andreas Wieser","year":"2019","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gold et al., 1998] Steven Gold, Anand Rangarajan, Chien- Ping Lu, Suguna Pappu, and Eric Mjolsness. New algo- rithms for 2d and 3d point matching: pose estimation and correspondence. Pattern Recognition, 31(8):1019-1031, 1998.","title":"New algorithms for 2d and 3d point matching: pose estimation and correspondence","authors":"Steven Gold, Anand Rangarajan, Chien-Ping Lu, Suguna Pappu, Eric Mjolsness","year":"1998","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Huang et al., 2020] Xiaoshui Huang, Guofeng Mei, and Jian Zhang. Feature-metric registration: A fast semi- supervised approach for robust point cloud registra- tion without correspondences. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11366-11374, 2020.","title":"Feature-metric registration: A fast semisupervised approach for robust point cloud registration without correspondences","authors":"Huang","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Huang et al., 2021] Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov, Andreas Wieser, and Konrad Schindler. Preda- tor: Registration of 3d point clouds with low overlap. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4267-4276, 2021.","title":"Predator: Registration of 3d point clouds with low overlap","authors":"Huang","year":"2021","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Jens et al., 2019] Behley Jens, Garbade Martin, Milioto An- dres, Quenzel Jan, Behnke Sven, Stachniss Cyrill, and Gall Jurgen. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.","title":"Semantickitti: A dataset for semantic scene understanding of lidar sequences","authors":"Jens","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Johnson and Hebert, 1999] Andrew E Johnson and Martial Hebert. Using spin images for efficient object recogni- tion in cluttered 3d scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5):433-449, 1999.","title":"Using spin images for efficient object recognition in cluttered 3d scenes","authors":"Andrew E, Johnson, Martial Hebert","year":"1999","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Li et al., 2020] Jiahao Li, Changhao Zhang, Ziyao Xu, Hangning Zhou, and Chi Zhang. Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration. In Proceedings of the European Conference on Computer Vi- sion, pages 378-394. Springer, 2020.","title":"Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration","authors":"Jiahao Li, Changhao Zhang, Ziyao Xu, Hangning Zhou, Chi Zhang","year":"2020","venue":"Proceedings of the European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lu et al., 2021] Fan Lu, Guang Chen, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, and Rongqi Gu. Hregnet: A hierarchical network for large-scale outdoor lidar point cloud registration. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 16014- 16023, 2021.","title":"Hregnet: A hierarchical network for large-scale outdoor lidar point cloud registration","authors":"Fan Lu, Guang Chen, Yinlong Liu, Lijun Zhang, Sanqing Qu, Shu Liu, Rongqi Gu","year":"2021","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lucas and Kanade, 1981] Bruce D Lucas and Takeo Kanade. An iterative image registration technique with an application to stereo vision. In Proceedings of DARPA Im- age Understanding Workshop, pages 121-130. Vancouver, British Columbia, 1981.","title":"An iterative image registration technique with an application to stereo vision","authors":"D Bruce, Takeo Lucas, Kanade","year":"1981","venue":"Proceedings of DARPA Image Understanding Workshop","doi":null,"url":null,"internal_url":"/proceedings/1981/127"},{"position":21,"raw":"[Pais et al., 2020] G Dias Pais, Srikumar Ramalingam, Venu Madhav Govindu, Jacinto C Nascimento, Rama Chellappa, and Pedro Miraldo. 3dregnet: A deep neural network for 3d point registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 7193-7203, 2020.","title":"3dregnet: A deep neural network for 3d point registration","authors":"Srikumar Dias Pais, Ramalingam, Madhav Venu, Jacinto C Govindu, Rama Nascimento, Pedro Chellappa, Miraldo","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Perfecto et al., 2017] Cristina Perfecto, Javier Del Ser, and Mehdi Bennis. Millimeter-wave v2v communications: Distributed association and beam alignment. IEEE J-SAC, 35(9):2148-2162, 2017.","title":"Millimeter-wave v2v communications: Distributed association and beam alignment","authors":"Cristina Perfecto, Javier Del Ser, Mehdi Bennis","year":"2017","venue":"IEEE J-SAC","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Poiesi and Boscaini, 2021] Fabio Poiesi and Davide Boscaini. Distinctive 3d local deep descriptors. In Proceedings of the International Conference on Pattern Recognition, pages 5720-5727. IEEE, 2021.","title":"Distinctive 3d local deep descriptors","authors":"Fabio Poiesi, Davide Boscaini","year":"2021","venue":"Proceedings of the International Conference on Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Qi et al., 2017] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 652-660, 2017.","title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","authors":"Hao Charles R Qi, Kaichun Su, Leonidas J Mo, Guibas","year":"2017","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Qin et al., 2022] Zheng Qin, Hao Yu, Changjian Wang, Yu- lan Guo, Yuxing Peng, and Kai Xu. Geometric transformer for fast and robust point cloud registration. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pat- tern Recognition, pages 11143-11152, 2022.","title":"Geometric transformer for fast and robust point cloud registration","authors":"Zheng Qin, Hao Yu, Changjian Wang, Yulan Guo, Yuxing Peng, Kai Xu","year":"2022","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Rusu et al., 2009] Radu Bogdan Rusu, Nico Blodow, and Michael Beetz. Fast point feature histograms (fpfh) for 3d registration. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 3212- 3217. IEEE, 2009.","title":"Fast point feature histograms (fpfh) for 3d registration","authors":"Bogdan Radu, Nico Rusu, Michael Blodow, Beetz","year":"2009","venue":"Proceedings of the IEEE International Conference on Robotics and Automation","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Sarode et al., 2019] Vinit Sarode, Xueqian Li, Hunter Go- forth, Yasuhiro Aoki, Rangaprasad Arun Srivatsan, Simon Lucey, and Howie Choset. Pcrnet: Point cloud regis- tration network using pointnet encoding. arXiv preprint arXiv:1908.07906, 2019.","title":"Pcrnet: Point cloud registration network using pointnet encoding","authors":"Xueqian Vinit Sarode, Hunter Li, Yasuhiro Goforth, Rangaprasad Aoki, Simon Arun Srivatsan, Howie Lucey, Choset","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Sun et al., 2020] Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, and Yichen Wei. Circle loss: A unified perspective of pair similarity optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6398-6407, 2020.","title":"Circle loss: A unified perspective of pair similarity optimization","authors":"Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, Yichen Wei","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Thomas et al., 2019] Hugues Thomas, Charles R Qi, Jean- Emmanuel Deschaud, Beatriz Marcotegui, Franc ¸ois Goulette, and Leonidas J Guibas. Kpconv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6411-6420, 2019.","title":"Kpconv: Flexible and deformable convolution for point clouds","authors":"Hugues Thomas, Charles R Qi, Jean-Emmanuel Deschaud, Beatriz Marcotegui, Leonidas J Franc ¸ois Goulette, Guibas","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Tombari et al., 2010] Federico Tombari, Samuele Salti, and Luigi Di Stefano. Unique signatures of histograms for lo- cal surface description. In Proceedings of the European Conference on Computer Vision, pages 356-369. Springer, 2010.","title":"Unique signatures of histograms for local surface description","authors":"Federico Tombari, Samuele Salti, Luigi Di, Stefano","year":"2010","venue":"Proceedings of the European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang and Solomon, 2019a] Yue Wang and Justin M Solomon. Deep closest point: Learning representations for point cloud registration. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3523-3532, 2019.","title":"Deep closest point: Learning representations for point cloud registration","authors":"Wang, Solomon ; A] Yue Wang, Justin M Solomon","year":"2019","venue":"Proceedings of the IEEE/CVF International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Wang and Solomon, 2019b] Yue Wang and Justin M Solomon. Prnet: Self-supervised learning for partial-to- partial registration. arXiv preprint arXiv:1910.12240, 2019.","title":"Prnet: Self-supervised learning for partial-topartial registration","authors":"Wang, Solomon ;, Yue Wang, Justin M Solomon","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Wang et al., 2020] Song Wang, Jingqi Huang, and Xinyu Zhang. Demystifying millimeter-wave v2x: Towards ro- bust and efficient directional connectivity under high mo- bility. In Proceedings of the ACM MobiCom, 2020.","title":"Demystifying millimeter-wave v2x: Towards robust and efficient directional connectivity under high mobility","authors":"Wang","year":"2020","venue":"Proceedings of the ACM MobiCom","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Weixin et al., 2019] Lu Weixin, Wan Guowei, Zhou Yao, Fu Xiangyu, Yuan Pengfei, and Song Shiyu. Deepicp: An end-to-end deep neural network for 3d point cloud regis- tration. arXiv preprint arXiv:1905.04153, 2019.","title":"Deepicp: An end-to-end deep neural network for 3d point cloud registration","authors":"Lu Weixin, Wan Guowei, Zhou Yao, Fu Xiangyu, Yuan Pengfei, Song Shiyu","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yew and Lee, 2018] Zi Jian Yew and Gim Hee Lee. 3dfeat- net: Weakly supervised local 3d features for point cloud registration. In Proceedings of the European Conference on Computer Vision, pages 607-623, 2018.","title":"3dfeatnet: Weakly supervised local 3d features for point cloud registration","authors":"Jian Zi, Gim Yew, Lee Hee","year":"2018","venue":"Proceedings of the European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yew and Lee, 2020] Zi Jian Yew and Gim Hee Lee. Rpm- net: Robust point matching using learned features. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11824-11833, 2020.","title":"Rpmnet: Robust point matching using learned features","authors":"Jian Zi, Gim Yew, Lee Hee","year":"2020","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yu et al., 2021] Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, and Slobodan Ilic. Cofinet: Reliable coarse- to-fine correspondences for robust pointcloud registra- tion. Advances in Neural Information Processing Systems, 34:23872-23884, 2021.","title":"Cofinet: Reliable coarseto-fine correspondences for robust pointcloud registration","authors":"Hao Yu, Fu Li, Mahdi Saleh, Benjamin Busam, Slobodan Ilic","year":"2021","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zeng et al., 2017] Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, and Thomas Funkhouser. 3dmatch: Learning local geometric descrip- tors from rgb-d reconstructions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1802-1811, 2017.","title":"3dmatch: Learning local geometric descriptors from rgb-d reconstructions","authors":"Andy Zeng, Shuran Song, Matthias Nießner, Matthew Fisher, Jianxiong Xiao, Thomas Funkhouser","year":"2017","venue":"Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}