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Found 5 papers, 1 papers with code
Self-Supervised Depth Completion Guided by 3D Perception and Geometry Consistency
Depth completion, aiming to predict dense depth maps from sparse depth measurements, plays a crucial role in many computer vision related applications.
Sparse3D: Distilling Multiview-Consistent Diffusion for Object Reconstruction from Sparse Views
While recent techniques employ image diffusion models for generating plausible images at novel viewpoints or for distilling pre-trained diffusion priors into 3D representations using score distillation sampling (SDS), these methods often struggle to simultaneously achieve high-quality, consistent, and detailed results for both novel-view synthesis (NVS) and geometry.
SC-NeuS: Consistent Neural Surface Reconstruction from Sparse and Noisy Views
The recent neural surface reconstruction by volume rendering approaches have made much progress by achieving impressive surface reconstruction quality, but are still limited to dense and highly accurate posed views.
MonoNeuralFusion: Online Monocular Neural 3D Reconstruction with Geometric Priors
This paper introduces a novel neural implicit scene representation with volume rendering for high-fidelity online 3D scene reconstruction from monocular videos.
DI-Fusion: Online Implicit 3D Reconstruction with Deep Priors
Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors.
