Prosformer: Accurate Surface Reconstruction for Sparse Profilometer Measurement with Transformer
ID:68 View Protection:PUBLIC Updated Time:2022-12-22 10:02:51 Hits:156 Poster Presentation

Start Time:Pending (Asia/Shanghai)


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Surface micro-structure measurement is significant for precision manufacturing. However, existing stylus profilometer is inefficient, and sparse line-scan measurement can’t support accurate surface description. To improve the reconstruction performance, we propose a high-accurate reconstruction method  with sparse line-scan measurement based on attention mechanism. We first arrange the sparse-line measurement in the 2D matrix and crop as patch region. Then we utilize transformer to construct semantic relationships between patches and assign  new weights to each patch to accurately model the structural relationships of target region and perform feature extraction, where the self-attention can enhance the description of local details while cross-attention will interact with global information. Finally, a fully connected network as a decoder is adopted to  reconstruct accurate surface details with complete geometric representation. We refer to this model as Prosformer. Furthermore, we simulate a larger-scale surface micro-structure dataset to drive the training process and measure micro-structures to valid Prosformer. Experiments show proposed method can effectively restore complex surface details.
surface measurement;profilometer;neural networks
Jieji Ren
PhD Candidate Shanghai JiaoTong University

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Important Dates

15th August 2022 25th September 2022 - Manuscript Submission
15th October 2022 - Acceptance Notification
1st November 2022 - Camera Ready Submission
1st November 2022 10th November 2022Early Bird Registration

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