Non-rigid object reconstruction based on multiview images: A survey

Liu, Zhi; Wang, Hongjun; Li, Qingli; Jun, Shi

doi:10.1109/cisp.2010.5648225

Cited by 2 publications

(3 citation statements)

References 29 publications

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“…Additionally, there are studies exploring view-dependent reflective appearance rendering by decomposing the scene into multiple physical components such as shape, reflectivity, and lighting. However, Ge et al [10] In recent research, a novel neural reconstruction method named NeRO [11] is proposed, which can accurately reconstruct the geometry of reflective objects without knowing the ambient lighting conditions and object masks. The core idea of this method involves incorporating the explicit integration of the rendering equation into a neural reconstruction framework, significantly enhancing the representational power of the neural implicit function, enabling it to capture high-frequency specular color changes.…”

Section: Reflective Object Reconstructionmentioning

confidence: 99%

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A Reflective Object Reconstruction Method Based on Neural Radiance Field

Li,

Yu,

Zhang

2024

AJST

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We propose a neural rendering-based 3D reconstruction method for reconstructing the geometry and BRDF of reflective objects from multi-view images captured in unknown environments. Multi-view reconstruction of reflective objects is extremely challenging because specular reflections are view-dependent and therefore violate multi-view consistency, which is the cornerstone of most multi-view reconstruction methods. Recent neural rendering techniques can model the interaction between ambient light and object surfaces to adapt to view-dependent reflections, making it possible to reconstruct reflective objects from multi-view images. However, accurately modeling ambient light in neural rendering is difficult. We propose a two-step approach to solve this problem. First, by introducing view-dependent photometric losses, our method accurately reconstructs the geometry of reflective objects. Then, with the object geometry fixed, we use more accurate sampling to recover the BRDF of the ambient light and object. Experiments show that our method is able to accurately reconstruct the geometry and BRDF of reflective objects from only RGB images without knowing the ambient light and object masks.

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Section: Reflective Object Reconstructionmentioning

confidence: 99%

“…First, the reflection score needs to be defined in order to identify the pixels corresponding to the reflective surface. The same method used by NeRF-W [11] is to make the rendering color of the light obey the Gaussian distribution:…”

Section: Geometry Reconstructionmentioning

confidence: 99%

A Reflective Object Reconstruction Method Based on Neural Radiance Field

Li,

Yu,

Zhang

2024

AJST

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“…Modern methods for estimating 3D models usually consist of three steps: reconstruction of surface parts, matching and stitching. The first step can be done by different techniques such as laser scanners [1], structured light projection [2], time of flight (TOF) cameras [3] or multi-frame reconstruction [4,5]. Matching is necessary in order to position all surface parts in the common coordinate system.…”

Section: Introductionmentioning

confidence: 99%

Range images registration by correction of perspective deformations and camera calibration

Lamovsky

2013

2013 IEEE International Conference on Image Processing

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The problem of registration of perspectively deformed range images presented in this paper. The deformation correction is considered as a calibration correction of the correspondent cameras. An algorithm for an affine transformation compensation is presented. We focus on the registration of high dimensional human head range images. Our aim is to stitch the corrected surfaces in order to estimate of full head model. A modification of the ICP algorithm is described. The latter is aimed at estimating affine transformation.

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Non-rigid object reconstruction based on multiview images: A survey

Cited by 2 publications

References 29 publications

A Reflective Object Reconstruction Method Based on Neural Radiance Field

A Reflective Object Reconstruction Method Based on Neural Radiance Field

Range images registration by correction of perspective deformations and camera calibration

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