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

## TL;DR

The paper introduces T-MLP, a novel neural network architecture that enables efficient multi-scale level-of-detail signal representation by attaching residual-refining tails to each hidden layer, outperforming existing methods.

## Contribution

We propose T-MLP, a modified MLP with attached tails at each layer for native multi-scale level-of-detail signal modeling, trained with single-resolution supervision.

## Key findings

- T-MLP outperforms existing neural LoD baselines across various tasks.
- T-MLP effectively models signals at multiple levels of detail.
- The architecture enables residual refinement at each layer for improved accuracy.

## 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.