Efficient Minimal-Surface Regularization of Perspective Depth Maps in Variational Stereo
We propose a method for dense three-dimensional surface reconstruction that leverages the strengths of shape-based approaches, by imposing regularization that respects the geometry of the surface, and the strength of depth-map-based stereo, by avoiding costly computation of surface topology. The result is a near real-time variational reconstruction algorithm free of the staircasing artifacts that affect depth-map and plane-sweeping approaches. This is made possible by exploiting the gauge ambiguity to design a novel representation of the regularizer that is linear in the parameters and hence amenable to be optimized with state-of-the-art primal-dual numerical schemes.
Code (0)
등록된 구현이 없습니다.
Tasks
Surface ReconstructionSimilar Papers 제목 키워드 기반
Perspective-aware fusion of incomplete depth maps and surface normals for accurate 3D reconstruction
We address the problem of reconstructing 3D surfaces from depth and surface normal maps acquired by a sensor system based on a single perspective camera. Depth and normal maps can be obtained through techniques such as s…
3D ReconstructionEndo-4DGS: Endoscopic Monocular Scene Reconstruction with 4D Gaussian Splatting
In the realm of robot-assisted minimally invasive surgery, dynamic scene reconstruction can significantly enhance downstream tasks and improve surgical outcomes. Neural Radiance Fields (NeRF)-based methods have recently …
Depth EstimationDynamic ReconstructionMonocular Depth EstimationMonocular Reconstruction+2Gaussian Set Surface Reconstruction through Per-Gaussian Optimization
3D Gaussian Splatting (3DGS) effectively synthesizes novel views through its flexible representation, yet fails to accurately reconstruct scene geometry. While modern variants like PGSR introduce additional losses to ens…
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera
We propose an approach for 3D reconstruction and segmentation of a single object placed on a flat surface from an input video. Our approach is to perform dense depth map estimation for multiple views using a proposed obj…
3D ReconstructionDepth EstimationObjectObject Reconstruction+2Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance
Pseudo depth maps are depth map predicitions which are used as ground truth during training. In this paper we leverage pseudo depth maps in order to segment objects of classes that have never been seen during training. T…
ObjectSemantic Segmentation