Pith. sign in

REVIEW 5 cited by

Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2303.02091 v2 pith:BMA4QHSS submitted 2023-03-03 cs.CV

Jiaxiang Tang , Hang Zhou , Xiaokang Chen , Tianshu Hu , Errui Ding , Jingdong Wang , Gang Zeng This is my paper · ORCID
classification cs.CV
keywords renderingmeshnerfsurfaceappearancegeometryimagesmeshes
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction. However, their implicit volumetric representations differ significantly from the widely-adopted polygonal meshes and lack support from common 3D software and hardware, making their rendering and manipulation inefficient. To overcome this limitation, we present a novel framework that generates textured surface meshes from images. Our approach begins by efficiently initializing the geometry and view-dependency decomposed appearance with a NeRF. Subsequently, a coarse mesh is extracted, and an iterative surface refining algorithm is developed to adaptively adjust both vertex positions and face density based on re-projected rendering errors. We jointly refine the appearance with geometry and bake it into texture images for real-time rendering. Extensive experiments demonstrate that our method achieves superior mesh quality and competitive rendering quality.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Squeeze3D: Your 3D Generation Model is Secretly an Extreme Neural Compressor

    cs.GR 2025-06 conditional novelty 6.0 of 10

    Two small mapping networks connect a frozen 3D encoder to a frozen 3D generator, so the generator decompresses objects from latent codes as small as 3 KB, achieving up to 2187x compression on meshes.

  2. GURecon: Learning Detailed 3D Geometric Uncertainties for Neural Surface Reconstruction

    cs.CV 2024-12 conditional novelty 6.0 of 10

    GURecon distills multi-view photometric consistency into a continuous 3D geometric uncertainty field for neural surfaces, improving uncertainty estimation and incremental reconstruction.

  3. Proc-GS: Procedural Building Generation for City Assembly with 3D Gaussians

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Proc-GS constrains 3D Gaussian Splatting with procedural code to extract reusable building assets and assemble new buildings and cities.

  4. AGS-Mesh: Adaptive Gaussian Splatting and Meshing with Geometric Priors for Indoor Room Reconstruction Using Smartphones

    cs.CV 2024-11 conditional novelty 6.0 of 10

    Adaptive filtering of noisy phone depth and monocular normal priors improves Gaussian splatting based 3D indoor reconstruction and mesh extraction.

  5. Neural Surface Priors for Editable Gaussian Splatting

    cs.CV 2024-11 conditional novelty 6.0 of 10

    A neural-surface-guided Gaussian splatting pipeline propagates mesh edits through a triangle soup proxy, enabling mesh-based editing of reconstructed scenes.

Pith tools