用带尾部的MLP实现多细节层级信号表示，仅需单分辨率数据训练
T-MLP: Tailed Multi-Layer Perceptron for Level-of-Detail Signal Representation
- 在每层MLP后加一个残差修正分支（尾部）
- 多层输出累积可生成不同细节层次的信号，精度优于现有方法
- 适合图像、3D形状等多尺度信号建模任务
细节层级（LoD）表示对高效建模与传输图像、3D形状等信号至关重要。本文提出一种新型网络架构——带尾多层感知机（T-MLP），通过在传统多层感知机（MLP）的每一隐藏层后添加一个输出分支（称为尾部），扩展其能力。每个尾部负责修正当前预测与真实信号之间的残差，使得各层输出累加后可对应不同细节层级的信号。该设计仅需单分辨率信号作为监督，即可实现多尺度建模。大量实验表明，T-MLP在多种信号表示任务中均超越现有神经网络类LoD基线方法。
原文摘要 · Abstract (English)
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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