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Found 22 papers, 7 papers with code
CARE Transformer: Mobile-Friendly Linear Visual Transformer via Decoupled Dual Interaction
In this paper, we propose a new de\textbf{C}oupled du\textbf{A}l-interactive linea\textbf{R} att\textbf{E}ntion (CARE) mechanism, revealing that features' decoupling and interaction can fully unleash the power of linear attention.
Mining and Transferring Feature-Geometry Coherence for Unsupervised Point Cloud Registration
Point cloud registration, a fundamental task in 3D vision, has achieved remarkable success with learning-based methods in outdoor environments.
Few-shot NeRF by Adaptive Rendering Loss Regularization
In this work, we reveal that there exists an inconsistency between the frequency regularization of PE and rendering loss.
MVGamba: Unify 3D Content Generation as State Space Sequence Modeling
Recent 3D large reconstruction models (LRMs) can generate high-quality 3D content in sub-seconds by integrating multi-view diffusion models with scalable multi-view reconstructors.
GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections.
PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction
In this work, we introduce PGAHum, a prior-guided geometry and appearance learning framework for high-fidelity animatable human reconstruction.
Diffusion Time-step Curriculum for One Image to 3D Generation
Score distillation sampling~(SDS) has been widely adopted to overcome the absence of unseen views in reconstructing 3D objects from a \textbf{single} image.
Precise-Physics Driven Text-to-3D Generation
However, existing generative methods mostly focus on geometric or visual plausibility while ignoring precise physics perception for the generated 3D shapes.
TaylorGrid: Towards Fast and High-Quality Implicit Field Learning via Direct Taylor-based Grid Optimization
In this paper, we aim for both fast and high-quality implicit field learning, and propose TaylorGrid, a novel implicit field representation which can be efficiently computed via direct Taylor expansion optimization on 2D or 3D grids.
PSDF: Prior-Driven Neural Implicit Surface Learning for Multi-view Reconstruction
While NISR has shown impressive results on simple scenes, it remains challenging to recover delicate geometry from uncontrolled real-world scenes which is caused by its underconstrained optimization.
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor Scenes
In this work we present P\textsuperscript 2 NeRF to capture global and hierarchical geometry consistency priors from pretrained models thus facilitating few-shot NeRFs in 360^\circ outward-facing indoor scenes.
PR-NeuS: A Prior-based Residual Learning Paradigm for Fast Multi-view Neural Surface Reconstruction
In this work, we propose a prior-based residual learning paradigm for fast multi-view neural surface reconstruction.
SPEAL: Skeletal Prior Embedded Attention Learning for Cross-Source Point Cloud Registration
To the best of our knowledge, our approach is the first to facilitate point cloud registration with skeletal geometric priors.
PG-NeuS: Robust and Efficient Point Guidance for Multi-View Neural Surface Reconstruction
Recently, learning multi-view neural surface reconstruction with the supervision of point clouds or depth maps has been a promising way.
Hierarchical Prior Mining for Non-local Multi-View Stereo
3) A Hierarchical Prior Mining (HPM) framework, which is used to mine extensive non-local prior information at different scales to assist 3D model recovery, this strategy can achieve a considerable balance between the reconstruction of details and low-textured areas.
Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction
Recently, neural implicit surfaces learning by volume rendering has become popular for multi-view reconstruction.
Non-local Recurrent Regularization Networks for Multi-view Stereo
However, existing recurrent methods only model the local dependencies in the depth domain, which greatly limits the capability of capturing the global scene context along the depth dimension.
PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network
We present a pixelwise visibility network to learn the visibility information for different neighboring images before computing the multi-view similarity, and then construct an adaptive weighted cost volume with the visibility information.
Learning Inverse Depth Regression for Multi-View Stereo with Correlation Cost Volume
This can be attributed to the memory-consuming cost volume representation and inappropriate depth inference.
Planar Prior Assisted PatchMatch Multi-View Stereo
In detail, we utilize a probabilistic graphical model to embed planar models into PatchMatch multi-view stereo and contribute a novel multi-view aggregated matching cost.
Ranked #1 on
Multi-View 3D Reconstruction
on ETH3D
Multi-Scale Geometric Consistency Guided Multi-View Stereo
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.
Ranked #2 on
Multi-View 3D Reconstruction
on ETH3D
Multi-View Stereo with Asymmetric Checkerboard Propagation and Multi-Hypothesis Joint View Selection
In computer vision domain, how to fast and accurately perform multiview stereo (MVS) is still a challenging problem.
