Towards End-to-End Neuromorphic Voxel-based 3D Object Reconstruction Without Physical Priors

2025-01-01

AI Summary

Existing neuromorphic camera-based 3D reconstruction methods rely on physical priors—such as motion or depth models—resulting in complex pipelines and limited robustness. This paper proposes the first prior-free, end-to-end, voxel-level monocular 3D reconstruction framework for event cameras. Our method eliminates all physical assumptions and directly recovers dense geometry from raw event streams. Specifically: (1) we introduce a prior-free reconstruction paradigm; (2) we design a novel event representation that enhances edge response; and (3) we establish an adaptive binarization threshold selection criterion grounded in reconstruction quality optimality. The entire pipeline operates on voxel grids with differentiable, trainable feature enhancement and geometric decoding. Experiments demonstrate a 54.6% improvement in reconstruction accuracy over state-of-the-art baselines, with substantial gains in completeness and robustness under high-speed motion and low-texture conditions.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Neuromorphic cameras, also known as event cameras, are asynchronous brightness-change sensors that can capture extremely fast motion without suffering from motion blur, making them particularly promising for 3D reconstruction in extreme environments. However, existing research on 3D reconstruction using monocular neuromorphic cameras is limited, and most of the methods rely on estimating physical priors and employ complex multi-step pipelines. In this work, we propose an end-to-end method for dense voxel 3D reconstruction using neuromorphic cameras that eliminates the need to estimate physical priors. Our method incorporates a novel event representation to enhance edge features, enabling the proposed feature-enhancement model to learn more effectively. Additionally, we introduced Optimal Binarization Threshold Selection Principle as a guideline for future related work, using the optimal reconstruction results achieved with threshold optimization as the benchmark. Our method achieves a 54.6% improvement in reconstruction accuracy compared to the baseline method.

Problem

• Neuromorphic Photonics
• 3D Modeling
• Simplified Method

Innovation

• Neuromorphic Camera
• 3D Modeling
• Parameter Optimization Strategy