We lifted 11 functions out of this paper's own repositories and ran 7 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| kwea123/nerf_pl | pwc_unofficial | 7 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| average_poses | Ran | kwea123/nerf_pl/datasets/llff.py code served (permissive licence) · get_code("1c2cad6f866aa88e") |
| center_poses | Ran | kwea123/nerf_pl/datasets/llff.py code served (permissive licence) · get_code("75fcfe935ba0aa57") |
| get_ndc_rays | Ran | kwea123/nerf_pl/datasets/ray_utils.py code served (permissive licence) · get_code("e80f008922038bf9") |
| get_rays | Ran | kwea123/nerf_pl/datasets/ray_utils.py code served (permissive licence) · get_code("8d0bc33089db3e48") |
| mse | Ran | kwea123/nerf_pl/metrics.py code served (permissive licence) · get_code("c832923b546e9fb3") |
| normalize | Ran | kwea123/nerf_pl/datasets/llff.py code served (permissive licence) · get_code("aa33e7122eb26d8b") |
| psnr | Ran | kwea123/nerf_pl/metrics.py code served (permissive licence) · get_code("b5b388448f22219a") |
| f | Not yet run | kwea123/nerf_pl/extract_color_mesh.py code served (permissive licence) · get_code("7fb907fb8afead36") |
| get_ray_directions | Not yet run | kwea123/nerf_pl/datasets/ray_utils.py code served (permissive licence) · get_code("e3610399262edff6") |
| read_pfm | Not yet run | kwea123/nerf_pl/datasets/depth_utils.py code served (permissive licence) · get_code("6b687b02c05fe850") |
| ssim | Not yet run | kwea123/nerf_pl/metrics.py code served (permissive licence) · get_code("895ffdb66b75e1b4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model the density and color of a scene as a function of 3D coordinates. While NeRF works well on images of static subjects captured under controlled settings, it is incapable of modeling many ubiquitous, real-world phenomena in uncontrolled images, such as variable illumination or transient occluders. We introduce a series of extensions to NeRF to address these issues, thereby enabling accurate reconstructions from unstructured image collections taken from the internet. We apply our system, dubbed NeRF-W, to internet photo collections of famous landmarks, and demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2008.02268")
get_code_for_paper("2008.02268")
have("2008.02268")
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