Seeing Tree Structure from Vibration

TL;DR

This paper introduces a spectrum analysis method for reconstructing tree structures from vibrations, effectively distinguishing disconnected branches by their natural frequencies using a physics-based model and Bayesian inference.

Contribution

It presents a novel physics-based formulation of tree structure from vibration spectra and demonstrates its effectiveness through theoretical, simulated, and real-world experiments.

Findings

Successfully distinguishes disconnected branches by natural frequencies.

Accurately reconstructs hierarchical tree structures from real-world videos.

Outperforms existing methods in structure recognition from vibration signals.

Abstract

Humans recognize object structure from both their appearance and motion; often, motion helps to resolve ambiguities in object structure that arise when we observe object appearance only. There are particular scenarios, however, where neither appearance nor spatial-temporal motion signals are informative: occluding twigs may look connected and have almost identical movements, though they belong to different, possibly disconnected branches. We propose to tackle this problem through spectrum analysis of motion signals, because vibrations of disconnected branches, though visually similar, often have distinctive natural frequencies. We propose a novel formulation of tree structure based on a physics-based link model, and validate its effectiveness by theoretical analysis, numerical simulation, and empirical experiments. With this formulation, we use nonparametric Bayesian inference to reconstruct tree structure from both spectral vibration signals and appearance cues. Our model performs well in recognizing hierarchical tree structure from real-world videos of trees and vessels.