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inverse rendering?

35,456 görüntüleme • 1 gün önce •via X (Twitter)

11 Yorum

cognito profil fotoğrafı
cognito1 gün önce

You can run the same experiment using colmap + gaussian splat and get arguably better results

neilson profil fotoğrafı
neilson1 gün önce

some reasons this is slightly different 1. these rooms aren't real 2. any input view can be changed in realtime to change the entire scene

Marcin Kobylecki profil fotoğrafı
Marcin Kobylecki1 gün önce

I gave it a try, and I must admit I actually had quite a bit of fun with it. There is one issue, however: it lacks the production quality required for a film; the realism comes across better than the 3D animation style.

EX3D profil fotoğrafı
EX3D1 gün önce

Fucking incredible.

Gio Gargiulo profil fotoğrafı
Gio Gargiulo1 gün önce

What am I looking at?

professional biaser profil fotoğrafı
professional biaser1 gün önce

absolutely mental. literally alien tech.

Rowdy Burns profil fotoğrafı
Rowdy Burns1 gün önce

What software?

tommy profil fotoğrafı
tommy19 saat önce

fuck…

Phil Gara profil fotoğrafı
Phil Gara1 gün önce

Amazing!

metamike profil fotoğrafı
metamike1 gün önce

Tf is this

Broadstreet profil fotoğrafı
Broadstreet1 gün önce

Excellent

Benzer Videolar

MaterialFusion Enhancing Inverse Rendering with Material Diffusion Priors discuss: Recent works in inverse rendering have shown promise in using multi-view images of an object to recover shape, albedo, and materials. However, the recovered components often fail to render accurately under new lighting conditions due to the intrinsic challenge of disentangling albedo and material properties from input images. To address this challenge, we introduce MaterialFusion, an enhanced conventional 3D inverse rendering pipeline that incorporates a 2D prior on texture and material properties. We present StableMaterial, a 2D diffusion model prior that refines multi-lit data to estimate the most likely albedo and material from given input appearances. This model is trained on albedo, material, and relit image data derived from a curated dataset of approximately ~12K artist-designed synthetic Blender objects called BlenderVault. we incorporate this diffusion prior with an inverse rendering framework where we use score distillation sampling (SDS) to guide the optimization of the albedo and materials, improving relighting performance in comparison with previous work. We validate MaterialFusion's relighting performance on 4 datasets of synthetic and real objects under diverse illumination conditions, showing our diffusion-aided approach significantly improves the appearance of reconstructed objects under novel lighting conditions. We intend to publicly release our BlenderVault dataset to support further research in this field.

AK

22,959 görüntüleme • 2 yıl önce