Refining Geometry from Depth Sensors using IR Shading Images

TL;DR

This paper introduces a robust IR shading-based method to refine 3D mesh geometry from consumer depth sensors like Kinect, leveraging IR images to improve detail and accuracy in indoor environments.

Contribution

The paper presents a novel IR shading model and a geometry refinement technique that enhances Kinect-derived meshes using IR shading cues and multi-view data.

Findings

Improved mesh detail and accuracy demonstrated on Kinect I and II data

IR shading cues provide robustness against ambient light interference

User study confirms quality enhancement of refined meshes

Abstract

We propose a method to refine geometry of 3D meshes from a consumer level depth camera, e.g. Kinect, by exploiting shading cues captured from an infrared (IR) camera. A major benefit to using an IR camera instead of an RGB camera is that the IR images captured are narrow band images that filter out most undesired ambient light, which makes our system robust against natural indoor illumination. Moreover, for many natural objects with colorful textures in the visible spectrum, the subjects appear to have a uniform albedo in the IR spectrum. Based on our analyses on the IR projector light of the Kinect, we define a near light source IR shading model that describes the captured intensity as a function of surface normals, albedo, lighting direction, and distance between light source and surface points. To resolve the ambiguity in our model between the normals and distances, we utilize an initial 3D mesh from the Kinect fusion and multi-view information to reliably estimate surface details that were not captured and reconstructed by the Kinect fusion. Our approach directly operates on the mesh model for geometry refinement. We ran experiments on our algorithm for geometries captured by both the Kinect I and Kinect II, as the depth acquisition in Kinect I is based on a structured-light technique and that of the Kinect II is based on a time-of-flight (ToF) technology. The effectiveness of our approach is demonstrated through several challenging real-world examples. We have also performed a user study to evaluate the quality of the mesh models before and after our refinements.