Fast MRF Optimization with Application to Depth Reconstruction
We describe a simple and fast algorithm for optimizing Markov random fields over images. The algorithm performs block coordinate descent by optimally updating a horizontal or vertical line in each step. While the algorithm is not as accurate as state-of-the-art MRF solvers on traditional benchmark problems, it is trivially parallelizable and produces competitive results in a fraction of a second. As an application, we develop an approach to increasing the accuracy of consumer depth cameras. The presented algorithm enables high-resolution MRF optimization at multiple frames per second and substantially increases the accuracy of the produced range images.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SwiftNDC: Fast Neural Depth Correction for High-Fidelity 3D Reconstruction
Depth-guided 3D reconstruction has gained popularity as a fast alternative to optimization-heavy approaches, yet existing methods still suffer from scale drift, multi-view inconsistencies, and the need for substantial re…
3D ReconstructionROSEFusion: Random Optimization for Online Dense Reconstruction under Fast Camera Motion
Online reconstruction based on RGB-D sequences has thus far been restrained to relatively slow camera motions (<1m/s). Under very fast camera motion (e.g., 3m/s), the reconstruction can easily crumble even for the state-…
Pose TrackingFast Converging 3D Gaussian Splatting for 1-Minute Reconstruction
We present a fast 3DGS reconstruction pipeline designed to converge within one minute, developed for the SIGGRAPH Asia 3DGS Fast Reconstruction Challenge. The challenge consists of an initial round using SLAM-generated c…
Time of the Flight of the Gaussians: Optimizing Depth Indirectly in Dynamic Radiance Fields
We present a method to reconstruct dynamic scenes from monocular continuous-wave time-of-flight (C-ToF) cameras using raw sensor samples that achieves similar or better accuracy than neural volumetric approaches and is 1…
3D ReconstructionFast and Accurate Depth Estimation from Sparse Light Fields
We present a fast and accurate method for dense depth reconstruction from sparsely sampled light fields obtained using a synchronized camera array. In our method, the source images are over-segmented into non-overlapping…
Depth EstimationSuperpixels