We lifted 5 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| facebookresearch/eyefultower | pwc_unofficial | 4 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| check_robot_connectivity | Ran | facebookresearch/eyefultower/processing/eyeful_dock/camera_transfer.py pointer only (licence: MIT) · get_code("02b64db9334103ed") |
| get_capture_path | Ran | facebookresearch/eyefultower/processing/eyeful_dock/pipeline_utils.py pointer only (licence: MIT) · get_code("4c2c3166d6e1adb8") |
| get_data_path | Ran | facebookresearch/eyefultower/processing/eyeful_dock/pipeline_utils.py pointer only (licence: MIT) · get_code("87d9b2fd12450d01") |
| run | Ran | facebookresearch/eyefultower/processing/eyeful_dock/pipeline_utils.py pointer only (licence: MIT) · get_code("d5bd265dfd49d8f2") |
| read_detection_json | Not yet run | facebookresearch/eyefultower/processing/hdr-reconstruction/compute_whitebalance.py pointer only (licence: MIT) · get_code("a0d073b534517660") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present an end-to-end system for the high-fidelity capture, model reconstruction, and real-time rendering of walkable spaces in virtual reality using neural radiance fields. To this end, we designed and built a custom multi-camera rig to densely capture walkable spaces in high fidelity and with multi-view high dynamic range images in unprecedented quality and density. We extend instant neural graphics primitives with a novel perceptual color space for learning accurate HDR appearance, and an efficient mip-mapping mechanism for level-of-detail rendering with anti-aliasing, while carefully optimizing the trade-off between quality and speed. Our multi-GPU renderer enables high-fidelity volume rendering of our neural radiance field model at the full VR resolution of dual 2K$\times$2K at 36 Hz on our custom demo machine. We demonstrate the quality of our results on our challenging high-fidelity datasets, and compare our method and datasets to existing baselines. We release our dataset on our project website.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2311.02542")
get_code_for_paper("2311.02542")
have("2311.02542")
Connect an agent — have() is free.