RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal, considering them as a vector of radiances rendered under simulated, varying illumination. This re-parameterization enables the seamless integration of reflectance and normal maps as input data in neural volume rendering-based 3D reconstruction while preserving a single optimization objective. In contrast, recent multi-view photometric stereo (MVPS) methods depend on multiple, potentially conflicting objectives. Despite its apparent simplicity, our proposed approach outperforms state-of-the-art approaches in MVPS benchmarks across F-score, Chamfer distance, and mean angular error metrics. Notably, it significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility.
Code (1)
Tasks
3D ReconstructionMulti-View 3D ReconstructionSimilar Papers 제목 키워드 기반
Multi-view Surface Reconstruction Using Normal and Reflectance Cues
Achieving high-fidelity 3D surface reconstruction while preserving fine details remains challenging, especially in the presence of materials with complex reflectance properties and without a dense-view setup. In this pap…
Surface ReconstructionReconstructing Objects in-the-wild for Realistic Sensor Simulation
Reconstructing objects from real world data and rendering them at novel views is critical to bringing realism, diversity and scale to simulation for robotics training and testing. In this work, we present NeuSim, a novel…
DiversityDepth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
Recently, methods for neural surface representation and rendering, for example NeuS, have shown that learning neural implicit surfaces through volume rendering is becoming increasingly popular and making good progress. H…
Object ReconstructionSurface ReconstructionFine-detailed Neural Indoor Scene Reconstruction using multi-level importance sampling and multi-view consistency
Recently, neural implicit 3D reconstruction in indoor scenarios has become popular due to its simplicity and impressive performance. Previous works could produce complete results leveraging monocular priors of normal or …
3D ReconstructionIndoor Scene ReconstructionSurface ReconstructionMVCPS-NeuS: Multi-view Constrained Photometric Stereo for Neural Surface Reconstruction
Multi-view photometric stereo (MVPS) recovers a high-fidelity 3D shape of a scene by benefiting from both multi-view stereo and photometric stereo. While photometric stereo boosts detailed shape reconstruction it nec…
Surface Reconstruction