Xuesong Gao, Chuanqi Jiao, Ruidong Chen, Weijie Wang, Weizhi Nie
{"title":"Point- pc:通过因果推理,在先验知识指导下完成点云","authors":"Xuesong Gao, Chuanqi Jiao, Ruidong Chen, Weijie Wang, Weizhi Nie","doi":"10.1049/cit2.12379","DOIUrl":null,"url":null,"abstract":"<p>The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints. Numerous methods use a partial-to-complete framework, directly predicting missing components via global characteristics extracted from incomplete inputs. However, this makes detail recovery challenging, as global characteristics fail to provide complete missing component specifics. A new point cloud completion method named Point-PC is proposed. A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion. Concretely, a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format. The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain, enabling retrieval of analogous shapes from incomplete inputs. In addition, the authors employ backdoor adjustment to eliminate confounders, which are shape prior components sharing identical semantic structures with incomplete inputs. Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches. The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.</p>","PeriodicalId":46211,"journal":{"name":"CAAI Transactions on Intelligence Technology","volume":"10 4","pages":"1007-1018"},"PeriodicalIF":7.3000,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/cit2.12379","citationCount":"0","resultStr":"{\"title\":\"Point-PC: Point cloud completion guided by prior knowledge via causal inference\",\"authors\":\"Xuesong Gao, Chuanqi Jiao, Ruidong Chen, Weijie Wang, Weizhi Nie\",\"doi\":\"10.1049/cit2.12379\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints. Numerous methods use a partial-to-complete framework, directly predicting missing components via global characteristics extracted from incomplete inputs. However, this makes detail recovery challenging, as global characteristics fail to provide complete missing component specifics. A new point cloud completion method named Point-PC is proposed. A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion. Concretely, a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format. The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain, enabling retrieval of analogous shapes from incomplete inputs. In addition, the authors employ backdoor adjustment to eliminate confounders, which are shape prior components sharing identical semantic structures with incomplete inputs. Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches. 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Point-PC: Point cloud completion guided by prior knowledge via causal inference
The goal of point cloud completion is to reconstruct raw scanned point clouds acquired from incomplete observations due to occlusion and restricted viewpoints. Numerous methods use a partial-to-complete framework, directly predicting missing components via global characteristics extracted from incomplete inputs. However, this makes detail recovery challenging, as global characteristics fail to provide complete missing component specifics. A new point cloud completion method named Point-PC is proposed. A memory network and a causal inference model are separately designed to introduce shape priors and select absent shape information as supplementary geometric factors for aiding completion. Concretely, a memory mechanism is proposed to store complete shape features and their associated shapes in a key-value format. The authors design a pre-training strategy that uses contrastive learning to map incomplete shape features into the complete shape feature domain, enabling retrieval of analogous shapes from incomplete inputs. In addition, the authors employ backdoor adjustment to eliminate confounders, which are shape prior components sharing identical semantic structures with incomplete inputs. Experiments conducted on three datasets show that our method achieves superior performance compared to state-of-the-art approaches. The code for Point-PC can be accessed by https://github.com/bizbard/Point-PC.git.
期刊介绍:
CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.