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Junkai Deng

3 accepted papers

2025

UNIS: A Unified Framework for Achieving Unbiased Neural Implicit Surfaces in Volume Rendering

ICCV 2025poster

Reconstruction from multi-view images is a fundamental challenge in computer vision that has been extensively studied over the past decades. Recently, neural radiance fields have driven significant advancements, especially through methods using implicit functions and volume rendering, achieving high…

Cited by 0SourcePDFScholar
2024

2S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Images

CVPR 2024poster

Recently building on the foundation of neural radiance field various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned d…

2024

From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $\alpha$-NeuS

NeurIPS 2024poster

Traditional 3D shape reconstruction techniques from multi-view images, such as structure from motion and multi-view stereo, face challenges in reconstructing transparent objects. Recent advances in neural radiance fields and its variants primarily address opaque or transparent objects, encountering…