Unlocking Retail Innovation: 3D Digital Storebuilding with Multi‑Camera Vision

Overview

On September 16, Liang Guixing, an algorithm engineer at Suning Retail Technology Research Institute, presented a lecture titled “Exploration of Digital 3D Storebuilding Technology” as part of the store digitization empowerment series.

3D Storebuilding Technology

Digital 3D storebuilding combines various visual sensors in a scene, leveraging multi‑camera advantages and algorithms to model the entire environment. With GPU acceleration, it achieves high‑precision, real‑time, full‑scene reconstruction, creating a highly mirrored digital world of the physical store. This greatly increases transparency of the "people‑goods‑place" (人货场) factors and makes full‑lifecycle fine‑grained management possible.

Camera Selection and Calibration

Depth cameras serve as the eyes of the system. Three main depth‑camera technologies are compared: structured light, time‑of‑flight (ToF), and stereo vision. Structured light offers high resolution but is sensitive to ambient light; ToF provides longer range with less ambient interference but requires complex hardware; stereo vision is low‑cost but depends heavily on image quality. The project ultimately chose the ToF solution.

Calibration Methods

Accurate camera calibration is essential for reliable 3D reconstruction. Traditional calibration uses known patterns (e.g., Zhang’s chessboard), providing high precision and stability. Self‑calibration methods based on motion or scene constraints are less robust for this application. Active‑vision calibration offers linear solutions but is costly and unsuitable for fixed cameras in stores.

Full‑Scene Joint Calibration and Optimization

Since the store contains many heterogeneous sensors (depth and security cameras), a full‑scene joint calibration is performed. The process groups cameras around a reference depth camera, applies multi‑frame reprojection error iteration, and uses double‑camera calibration results as inputs. Global closed‑loop optimization and iterative closest‑point (ICP) refinement produce high‑quality, unified point‑cloud models.

Applications and Benefits

The resulting digital twin enables VR store tours, 360° customer modeling, and personalized services such as automated greetings, dynamic product recommendations, and intelligent checkout assistance. By analyzing pedestrian trajectories, dwell times, and item interactions, retailers can derive precise user profiles and tailor marketing strategies, ultimately enhancing the shopping experience.