Toward Depth Estimation Using Mask-Based Lensless Cameras

TL;DR

This paper introduces a novel method for depth and intensity estimation using mask-based lensless cameras, leveraging a light field model and a greedy algorithm to reconstruct 3D scenes from FlatCam measurements.

Contribution

It proposes a new imaging model and depth estimation algorithm specifically designed for FlatCam, enabling 3D scene reconstruction from lensless camera data.

Findings

Effective depth and intensity estimation demonstrated in simulations.

The light field model accurately captures the mapping from 3D scene to sensor.

The greedy algorithm efficiently searches the 3D volume for scene reconstruction.

Abstract

Recently, coded masks have been used to demonstrate a thin form-factor lensless camera, FlatCam, in which a mask is placed immediately on top of a bare image sensor. In this paper, we present an imaging model and algorithm to jointly estimate depth and intensity information in the scene from a single or multiple FlatCams. We use a light field representation to model the mapping of 3D scene onto the sensor in which light rays from different depths yield different modulation patterns. We present a greedy depth pursuit algorithm to search the 3D volume and estimate the depth and intensity of each pixel within the camera field-of-view. We present simulation results to analyze the performance of our proposed model and algorithm with different FlatCam settings.