Multi-Scale Geometric Consistency Guided Multi-View Stereo
In this paper, we propose an efficient multi-scale geometric consistency guided multi-view stereo method for accurate and complete depth map estimation. We first present our basic multi-view stereo method with Adaptive Checkerboard sampling and Multi-Hypothesis joint view selection (ACMH). It leverages structured region information to sample better candidate hypotheses for propagation and infer the aggregation view subset at each pixel. For the depth estimation of low-textured areas, we further propose to combine ACMH with multi-scale geometric consistency guidance (ACMM) to obtain the reliable depth estimates for low-textured areas at coarser scales and guarantee that they can be propagated to finer scales. To correct the erroneous estimates propagated from the coarser scales, we present a novel detail restorer. Experiments on extensive datasets show our method achieves state-of-the-art performance, recovering the depth estimation not only in low-textured areas but also in details.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth EstimationMulti-View 3D ReconstructionPoint CloudsSimilar Papers 제목 키워드 기반
Depth-Guided Metric-Aware Temporal Consistency for Monocular Video Human Mesh Recovery
Monocular video human mesh recovery faces fundamental challenges in maintaining metric consistency and temporal stability due to inherent depth ambiguities and scale uncertainties. While existing methods rely primarily o…
Computational EfficiencyHuman Mesh RecoveryGC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo
Traditional multi-view stereo (MVS) methods rely heavily on photometric and geometric consistency constraints, but newer machine learning-based MVS methods check geometric consistency across multiple source views only as…
3D ReconstructionMulti-View 3D ReconstructionPoint CloudsMetroGS: Efficient and Stable Reconstruction of Geometrically Accurate High-Fidelity Large-Scale Scenes
Recently, 3D Gaussian Splatting and its derivatives have achieved significant breakthroughs in large-scale scene reconstruction. However, how to efficiently and stably achieve high-quality geometric fidelity remains a co…
GeoNLF: Geometry guided Pose-Free Neural LiDAR Fields
Although recent efforts have extended Neural Radiance Fields (NeRF) into LiDAR point cloud synthesis, the majority of existing works exhibit a strong dependence on precomputed poses. However, point cloud registration met…
NeRFNovel View SynthesisPoint Cloud RegistrationPose EstimationLearning Geometric Representations from Videos for Spatial Intelligent Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to maintain geometric and spatial consistency across video frames. Given…
Spatial Reasoning