Multi-View Neural Surface Reconstruction with Structured Light
Chunyu Li, Taisuke Hashimoto, Eiichi Matsumoto, Hiroharu Kato

TL;DR
This paper introduces a novel multi-view 3D reconstruction method combining differentiable rendering with structured light to improve accuracy on textureless and shiny objects, reducing calibration efforts.
Contribution
It integrates structured light constraints into differentiable rendering, enabling accurate geometry and appearance learning and simultaneous camera pose optimization.
Findings
Outperforms conventional methods on synthetic and real data.
Achieves high reconstruction accuracy for textureless and shiny objects.
Reduces camera calibration efforts.
Abstract
Three-dimensional (3D) object reconstruction based on differentiable rendering (DR) is an active research topic in computer vision. DR-based methods minimize the difference between the rendered and target images by optimizing both the shape and appearance and realizing a high visual reproductivity. However, most approaches perform poorly for textureless objects because of the geometrical ambiguity, which means that multiple shapes can have the same rendered result in such objects. To overcome this problem, we introduce active sensing with structured light (SL) into multi-view 3D object reconstruction based on DR to learn the unknown geometry and appearance of arbitrary scenes and camera poses. More specifically, our framework leverages the correspondences between pixels in different views calculated by structured light as an additional constraint in the DR-based optimization of implicit…
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Taxonomy
TopicsAdvanced Vision and Imaging · Optical measurement and interference techniques · Robotics and Sensor-Based Localization
