T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914063810822144 |
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| author | Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ren, Siyu Liu, Yuan Hou, Junhui Wang, Wenping |
| author_facet | Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ren, Siyu Liu, Yuan Hou, Junhui Wang, Wenping |
| contents | Level-of-detail (LoD) representation is critical for efficiently modeling and transmitting various types of signals, such as images and 3D shapes. In this work, we propose a novel network architecture that enables LoD signal representation. Our approach builds on a modified Multi-Layer Perceptron (MLP), which inherently operates at a single scale and thus lacks native LoD support. Specifically, we introduce the Tailed Multi-Layer Perceptron (T-MLP), which extends the MLP by attaching an output branch, also called tail, to each hidden layer. Each tail refines the residual between the current prediction and the ground-truth signal, so that the accumulated outputs across layers correspond to the target signals at different LoDs, enabling multi-scale modeling with supervision from only a single-resolution signal. Extensive experiments demonstrate that our T-MLP outperforms existing neural LoD baselines across diverse signal representation tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_00066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation Yang, Chuanxiang Zhou, Yuanfeng Wei, Guangshun Ren, Siyu Liu, Yuan Hou, Junhui Wang, Wenping Machine Learning Graphics Image and Video Processing Level-of-detail (LoD) representation is critical for efficiently modeling and transmitting various types of signals, such as images and 3D shapes. In this work, we propose a novel network architecture that enables LoD signal representation. Our approach builds on a modified Multi-Layer Perceptron (MLP), which inherently operates at a single scale and thus lacks native LoD support. Specifically, we introduce the Tailed Multi-Layer Perceptron (T-MLP), which extends the MLP by attaching an output branch, also called tail, to each hidden layer. Each tail refines the residual between the current prediction and the ground-truth signal, so that the accumulated outputs across layers correspond to the target signals at different LoDs, enabling multi-scale modeling with supervision from only a single-resolution signal. Extensive experiments demonstrate that our T-MLP outperforms existing neural LoD baselines across diverse signal representation tasks. |
| title | T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation |
| topic | Machine Learning Graphics Image and Video Processing |
| url | https://arxiv.org/abs/2509.00066 |