Frequency-Based 3D Reconstruction of Transparent and Specular Objects
3D reconstruction of transparent and specular objects is a very challenging topic in computer vision. For transparent and specular objects, which have complex interior and exterior structures that can reflect and refract light in a complex fashion, it is difficult, if not impossible, to use either passive stereo or the traditional structured light methods to do the reconstruction. We propose a frequency-based 3D reconstruction method, which incorporates the frequency-based matting method. Similar to the structured light methods, a set of frequency-based patterns are projected onto the object, and a camera captures the scene. Each pixel of the captured image is analyzed along the time axis and the corresponding signal is transformed to the frequency-domain using the Discrete Fourier Transform. Since the frequency is only determined by the source that creates it, the frequency of the signal can uniquely identify the location of the pixel in the patterns. In this way, the correspondences between the pixels in the captured images and the points in the patterns can be acquired. Using a new labelling procedure, the surface of transparent and specular objects can be reconstructed with very encouraging results.
Code (0)
등록된 구현이 없습니다.
Tasks
3D ReconstructionImage MattingSimilar Papers 제목 키워드 기반
TransparentGS: Fast Inverse Rendering of Transparent Objects with Gaussians
The emergence of neural and Gaussian-based radiance field methods has led to considerable advancements in novel view synthesis and 3D object reconstruction. Nonetheless, specular reflection and refraction continue to pos…
3D Object ReconstructionInverse RenderingNovel View SynthesisObject Reconstruction+1SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Recent advancements in 3D robotic manipulation have improved grasping of everyday objects, but transparent and specular materials remain challenging due to depth sensing limitations. While several 3D reconstruction and d…
3D Object Reconstruction3D ReconstructionDepth CompletionObject Reconstruction+2NeuGrasp: Generalizable Neural Surface Reconstruction with Background Priors for Material-Agnostic Object Grasp Detection
Robotic grasping in scenes with transparent and specular objects presents great challenges for methods relying on accurate depth information. In this paper, we introduce NeuGrasp, a neural surface reconstruction method t…
Robotic GraspingSurface ReconstructionGAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects
Transparent and specular objects are frequently encountered in daily life, factories, and laboratories. However, due to the unique optical properties, the depth information on these objects is usually incomplete and inac…
Depth CompletionDepth EstimationDepth PredictionRobotic GraspingRT-GS: Gaussian Splatting with Reflection and Transmittance Primitives
Gaussian Splatting is a powerful tool for reconstructing diffuse scenes, but it struggles to simultaneously model specular reflections and the appearance of objects behind semi-transparent surfaces. These specular reflec…
Novel View Synthesis