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Evaluating Panoramic 3D Estimation in Indoor Lighting Analysis

Zining Cheng, Guanzhou Ji

TL;DR

This work investigates using panoramic 3D layout estimation to drive indoor lighting simulations, aiming to reduce manual modeling labor. It compares luminance outcomes from three pipelines—panoramic HDR photography, a 3D estimation model, and a detailed manually modeled geometry—across two sky conditions using a Radiance-based framework. The findings indicate that the 3D estimation approach can reliably support lighting analysis for the selected space, producing reasonable luminance predictions and glare metrics, though it tends to overestimate direct sunlight near windows due to simplified geometry. The study demonstrates the practicality of panoramic 3D estimation for efficient design exploration while highlighting limitations in geometry detail and outdoor-context representation, suggesting directions for broader scene testing and geographic variation.

Abstract

This paper presents the use of panoramic 3D estimation in lighting simulation. Conventional lighting simulation necessitates detailed modeling as input, resulting in significant labor effort and time cost. The 3D layout estimation method directly takes a single panorama as input and generates a lighting simulation model with room geometry and window aperture. We evaluate the simulation results by comparing the luminance errors between on-site High Dynamic Range (HDR) photographs, 3D estimation model, and detailed model in panoramic representation and fisheye perspective. Given the selected scene, the results demonstrate the estimated room layout is reliable for lighting simulation.

Evaluating Panoramic 3D Estimation in Indoor Lighting Analysis

TL;DR

This work investigates using panoramic 3D layout estimation to drive indoor lighting simulations, aiming to reduce manual modeling labor. It compares luminance outcomes from three pipelines—panoramic HDR photography, a 3D estimation model, and a detailed manually modeled geometry—across two sky conditions using a Radiance-based framework. The findings indicate that the 3D estimation approach can reliably support lighting analysis for the selected space, producing reasonable luminance predictions and glare metrics, though it tends to overestimate direct sunlight near windows due to simplified geometry. The study demonstrates the practicality of panoramic 3D estimation for efficient design exploration while highlighting limitations in geometry detail and outdoor-context representation, suggesting directions for broader scene testing and geographic variation.

Abstract

This paper presents the use of panoramic 3D estimation in lighting simulation. Conventional lighting simulation necessitates detailed modeling as input, resulting in significant labor effort and time cost. The 3D layout estimation method directly takes a single panorama as input and generates a lighting simulation model with room geometry and window aperture. We evaluate the simulation results by comparing the luminance errors between on-site High Dynamic Range (HDR) photographs, 3D estimation model, and detailed model in panoramic representation and fisheye perspective. Given the selected scene, the results demonstrate the estimated room layout is reliable for lighting simulation.
Paper Structure (17 sections, 4 equations, 10 figures, 1 table)

This paper contains 17 sections, 4 equations, 10 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Climate-Based Lighting Simulation
  4. 3D Layout Estimation
  5. HDR Photography
  6. Methodology
  7. Panoramic HDR Photography
  8. 3D Estimation
  9. Detailed Model
  10. Simulation Settings
  11. Results
  12. Panoramic Luminance Map
  13. Panoramic Error Analysis
  14. Fisheye Luminance Map
  15. Glare Metric
  16. ...and 2 more sections

Figures (10)

  • Figure 1: (left) HDR Photograph, (middle) 3D Estimation Model, and (right) Detailed Model.
  • Figure 2: (left) Spherical Coordinate and (right) Equirectanglar Representation.
  • Figure 3: Examples of the estimated 3D Layouts from different indoor panoramas.
  • Figure 4: Detailed 3D geometry of the selected room and view divisions for glare analysis.
  • Figure 5: Panoramic HDR photographs displayed as Low Dynamic Range (LDR) images and luminance maps generated from 3D estimation and detailed modeling on July 6th under clear sky conditions.
  • ...and 5 more figures