{"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/ashapeformer-semantics-guided-object-level","title":"AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via Transformers","arxiv_id":null,"date":"2023-01-01","proceeding":"CVPR 2023 1","authors":["Zechuan Li","Hongshan Yu","Zhengeng Yang","Tongjia Chen","Naveed Akhtar"],"abstract":"    3D object detection techniques commonly follow a pipeline that aggregates predicted object central point features to compute candidate points. However, these candidate points contain only positional information, largely ignoring the object-level shape information. This eventually leads to sub-optimal 3D object detection. In this work, we propose AShapeFormer, a semantics-guided object-level shape encoding module for 3D object detection. This is a plug-n-play module that leverages multi-head attention to encode object shape information. We also propose shape tokens and object-scene positional encoding to ensure that the shape information is fully exploited. Moreover, we introduce a semantic guidance sub-module to sample more foreground points and suppress the influence of background points for a better object shape perception. We demonstrate a straightforward enhancement of multiple existing methods with our AShapeFormer. Through extensive experiments on the popular SUN RGB-D and ScanNetV2 dataset, we show that our enhanced models are able to outperform the baselines by a considerable absolute margin of up to 8.1%. Code will be available at https://github.com/ZechuanLi/AShapeFormer    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2023/html/Li_AShapeFormer_Semantics-Guided_Object-Level_Active_Shape_Encoding_for_3D_Object_Detection_CVPR_2023_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2023/papers/Li_AShapeFormer_Semantics-Guided_Object-Level_Active_Shape_Encoding_for_3D_Object_Detection_CVPR_2023_paper.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":"ashapeformer-semantics-guided-object-level","repo_url":"https://github.com/zechuanli/ashapeformer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}