3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in semantic-part-detection
Use these libraries to find semantic-part-detection models and implementations
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This work proposes a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine- grained local details of 3D shapes from multiple rendered views and shows that this method outperforms other state-of-the-art methods.
This model is created on top of two Faster-RCNN models that share their features to perform a novel Attention-based feature fusion of related Object and Part features to get enhanced representations of both.
This paper presents an approach which can learn from a small annotated dataset containing a limited range of viewpoints and generalize to detect semantic parts for a much largerrange of viewpoints.
Adding a benchmark result helps the community track progress.