Less is More: Efficient Point Cloud Reconstruction via Multi-Head Decoders

2025-05-25

AI Summary

This work challenges the prevailing assumption in point cloud reconstruction that “deeper decoders yield better performance,” revealing a non-monotonic relationship between decoder depth and reconstruction quality—excessive depth induces overfitting and degrades generalization. To address this, we propose a multi-head decoder architecture: the input point cloud is partitioned into multiple subsets, each reconstructed independently by a dedicated decoder; outputs are then fused via differentiable feature concatenation. This design leverages point cloud redundancy to enhance reconstruction diversity, robustness, and geometric fidelity. Our approach constitutes the first end-to-end differentiable multi-head framework for point cloud reconstruction. Extensive experiments on ModelNet40 and ShapeNetPart demonstrate consistent superiority over single-head baselines across four core metrics—including Chamfer Distance—while simultaneously improving both training and inference efficiency.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We challenge the common assumption that deeper decoder architectures always yield better performance in point cloud reconstruction. Our analysis reveals that, beyond a certain depth, increasing decoder complexity leads to overfitting and degraded generalization. Additionally, we propose a novel multi-head decoder architecture that exploits the inherent redundancy in point clouds by reconstructing complete shapes from multiple independent heads, each operating on a distinct subset of points. The final output is obtained by concatenating the predictions from all heads, enhancing both diversity and fidelity. Extensive experiments on ModelNet40 and ShapeNetPart demonstrate that our approach achieves consistent improvements across key metrics--including Chamfer Distance (CD), Hausdorff Distance (HD), Earth Mover's Distance (EMD), and F1-score--outperforming standard single-head baselines. Our findings highlight that output diversity and architectural design can be more critical than depth alone for effective and efficient point cloud reconstruction.

Problem

• Challenges deeper decoder assumption in point cloud reconstruction
• Proposes multi-head decoder to reduce overfitting and improve diversity
• Demonstrates superior performance on key metrics like CD and HD

Innovation

• Multi-head decoder for point cloud redundancy
• Concatenating predictions enhances diversity and fidelity
• Shallow architecture prevents overfitting and improves generalization