Technical Report of 2023 ABO Fine-grained Semantic Segmentation Competition
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Abstract (arXiv)
In this report, we describe the technical details of our submission to the 2023 ABO Fine-grained Semantic Segmentation Competition, by Team "ZeyuDong" (username:ZeyuDong). The task is to predicate the semantic labels for the convex shape of five categories, which consist of high-quality, standardized 3D models of real products available for purchase online. By using DGCNN as the backbone to classify different structures of five classes, We carried out numerous experiments and found learning rate stochastic gradient descent with warm restarts and setting different rate of factors for various categories contribute most to the performance of the model. The appropriate method helps us rank 3rd place in the Dev phase of the 2023 ICCV 3DVeComm Workshop Challenge.
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References · 5
- 2019Simplifying Graph Convolutional Networkscited by 1,470
- 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
- 2017Dynamic Graph Convolutional Networkscited by 83
- 20173D Morphable Models as Spatial Transformer Networkscited by 12
- 2023HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labelingcited by 3
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