We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| barbararoessle/dense_depth_priors_nerf | canonical | 2 of 2 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| batchify | Ran | this paper's copy was not recorded; identical code first harvested from greatdrake/reparameterized-volume-sampling pointer only · get_code("fd57e365830d09de") |
| get_load_path | Ran | barbararoessle/dense_depth_priors_nerf/run_depth_completion.py code served (permissive licence) · get_code("a08c285fa6daffdf") |
| run_network | Ran | barbararoessle/dense_depth_priors_nerf/run_nerf.py code served (permissive licence) · get_code("1d5e7c8e9a1e7735") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Neural radiance fields (NeRF) encode a scene into a neural representation that enables photo-realistic rendering of novel views. However, a successful reconstruction from RGB images requires a large number of input views taken under static conditions - typically up to a few hundred images for room-size scenes. Our method aims to synthesize novel views of whole rooms from an order of magnitude fewer images. To this end, we leverage dense depth priors in order to constrain the NeRF optimization. First, we take advantage of the sparse depth data that is freely available from the structure from motion (SfM) preprocessing step used to estimate camera poses. Second, we use depth completion to convert these sparse points into dense depth maps and uncertainty estimates, which are used to guide NeRF optimization. Our method enables data-efficient novel view synthesis on challenging indoor scenes, using as few as 18 images for an entire scene.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2112.03288")
get_code_for_paper("2112.03288")
have("2112.03288")
Connect an agent — have() is free.