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arXiv · 2509.00066

T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation

Chuanxiang Yang·Yuanfeng Zhou·Guangshun Wei·Siyu Ren·Yuan Liu·Junhui Hou·Wenping Wang
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Abstract

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.

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Chuanxiang Yang, Yuanfeng Zhou, Guangshun Wei, Siyu Ren, Yuan Liu, Junhui Hou, Wenping Wang. 2025-08-26. T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation. https://arxiv.org/abs/2509.00066

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