Enhancement of Depth Value Approximation for 3D Image-Based Modelling using Noise Filtering and Inverse Perspective Mapping Techniques for Complex Object

Enhancement of Depth Value Approximation for 3D Image-Based Modelling using Noise Filtering and Inverse Perspective Mapping Techniques for Complex Object


  • Intan Syaherra Ramli Universiti Teknologi MARA
  • Rahmita Wirza OK RAhmat Universiti Putra Malaysia
  • Seng Beng Ng Universiti Putra Malaysia




Depth Value Approximation, Optical Flow, Trigonometry


This article proposes the methods to enhance the depth value approximation in 3D Image Based Modelling for complex object. Fundamentally, the fast and accurate depth value approximation is crucial as the 3D modelling used in virtual and augmented reality applications, reverse engineering, and the architecture. Therefore, the enhanced method must be robust against the challenges with noise, complexity, distortion and longer processing time. In this experiment, five small and complex objects were captured using a turntable, laptop, and a webcam. The feature points between images were tracked and matched using good features to tracks and Pyramidal Lucas Kanade's optical flow. Next, the depth value was approximated using trigonometry equation. To enhance the accuracy, the noise filtering, and Inverse Perspective Mapping (IPM) were introduced. The results show that the average error based on the approximated width and depth dimensions was 3.27% and 6.88% compared with the actual object. Furthermore, the processing speed was 1519 points per second. Therefore, this method enhanced the depth value approximation, which can be used to build the full texture 3D model in future.




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How to Cite

Ramli, I. S., OK Rahmat, R. W., & Ng, S. B. (2023). Enhancement of Depth Value Approximation for 3D Image-Based Modelling using Noise Filtering and Inverse Perspective Mapping Techniques for Complex Object. Journal of Computing Research and Innovation, 8(2), 246–264. https://doi.org/10.24191/jcrinn.v8i2.356



General Computing