Neural Surface Reconstruction Neus Vs

Media Summary: MERL Researchers Pedro Miraldo and Moitreya Chatterjee present his paper titled "A Probability-guided Sampler for A signed distance function (SDF) parametrized by an MLP is a common ingredient of Authors: Chen, Decai*; Zhang, Peng; Feldmann, Ingo; Schreer, Oliver; Eisert, Peter Description: Recent works on implicit

Neural Surface Reconstruction Neus Vs - Detailed Analysis & Overview

MERL Researchers Pedro Miraldo and Moitreya Chatterjee present his paper titled "A Probability-guided Sampler for A signed distance function (SDF) parametrized by an MLP is a common ingredient of Authors: Chen, Decai*; Zhang, Peng; Feldmann, Ingo; Schreer, Oliver; Eisert, Peter Description: Recent works on implicit

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Neural Surface Reconstruction (NeuS) vs. Photogrammetry

LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline

[CVPR 2026] Neu-PiG: Neural Preconditioned Grids for Fast Dynamic Surface Reconstruction

High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality

（CVPR-2026）Opti-NeuS: Neural Reconstruction for Dual-Layered Transparent and Opaque Objects

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces

Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Ref

Recovering Fine Details for Neural Implicit Surface Reconstruction

Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

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Neural Surface Reconstruction (NeuS) vs. Photogrammetry

Neural Surface Reconstruction (NeuS) vs. Photogrammetry

Neuralangelo: https://research.nvidia.com/labs/dir/neuralangelo/ Kiri Engine: https://www.kiriengine.app/web-version/ Android: ...

LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline

LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline

LightNeuS:

[CVPR 2026] Neu-PiG: Neural Preconditioned Grids for Fast Dynamic Surface Reconstruction

[CVPR 2026] Neu-PiG: Neural Preconditioned Grids for Fast Dynamic Surface Reconstruction

Poster Presentation CVPR 2026 Code: https://github.com/vc-bonn/neu-pig.

High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality

High-Fidelity Mask-free Neural Surface Reconstruction for Virtual Reality

This paper presents Hi-

（CVPR-2026）Opti-NeuS: Neural Reconstruction for Dual-Layered Transparent and Opaque Objects

（CVPR-2026）Opti-NeuS: Neural Reconstruction for Dual-Layered Transparent and Opaque Objects

3D reconstruction

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

[ECCV 2024] PS-NEUS: A Probability-guided Sampler for Neural Implicit Surface Rendering

MERL Researchers Pedro Miraldo and Moitreya Chatterjee present his paper titled "A Probability-guided Sampler for

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

NeuS2: Fast Learning of

PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces

PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces

A signed distance function (SDF) parametrized by an MLP is a common ingredient of

Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Ref

Ref-NeuS: Ambiguity-Reduced Neural Implicit Surface Learning for Multi-View Reconstruction with Ref

Ref-

Recovering Fine Details for Neural Implicit Surface Reconstruction

Recovering Fine Details for Neural Implicit Surface Reconstruction

Authors: Chen, Decai*; Zhang, Peng; Feldmann, Ingo; Schreer, Oliver; Eisert, Peter Description: Recent works on implicit

Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems

Neural Surface Reconstruction and Rendering for LiDAR-Visual Systems

This paper presents a unified

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

We develop a method that recovers the

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction. In ICCV, 2023.

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction. In ICCV, 2023.

We propose a fast