Henrik Kretzschmar, Matthew Tancik, Xinchen Yan, Sabeek Pradhan, Vincent Casser, B. Mildenhall, P. Srinivasan, J. Barron
We lifted 14 functions out of this paper's own repositories and ran 10 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 |
|---|---|---|
| dvlab-research/LargeScaleNeRFPytorch | pwc_unofficial | 10 of 14 |
| Function | Status | Where it lives |
|---|---|---|
| FourierGrid_compute_bbox_by_cam_frustrm_waymo | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/bbox_compute.py code served (permissive licence) · get_code("ab783a8f16a37718") |
| convert_to_ndc | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/camera_utils.py code served (permissive licence) · get_code("ce266e17c5088b07") |
| create_full_step_id | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/dmpigo.py code served (permissive licence) · get_code("eb2f4df4e13ab013") |
| filter_Block | Ran | dvlab-research/LargeScaleNeRFPytorch/eval_block_nerf.py code served (permissive licence) · get_code("cf4cfe9b84749e46") |
| filter_cam_info_by_index | Ran | dvlab-research/LargeScaleNeRFPytorch/eval_block_nerf.py code served (permissive licence) · get_code("756fbe4497eabee7") |
| get_rays | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/FourierGrid_model.py code served (permissive licence) · get_code("049307a781780caa") |
| get_rays_of_a_view | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/FourierGrid_model.py code served (permissive licence) · get_code("2703eb1cf816a1af") |
| intrinsic_matrix | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/camera_utils.py code served (permissive licence) · get_code("278d19a408119216") |
| ndc_rays | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/FourierGrid_model.py code served (permissive licence) · get_code("4366d225ac2fe16d") |
| split_rays | Ran | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/camera_utils.py code served (permissive licence) · get_code("c38d089d67862202") |
| FourierGrid_compute_bbox_by_cam_frustrm_mega | Not yet run | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/bbox_compute.py code served (permissive licence) · get_code("ba84c492683659b1") |
| batched_inference | Not yet run | dvlab-research/LargeScaleNeRFPytorch/eval_block_nerf.py code served (permissive licence) · get_code("bec3e39fd4aacd1e") |
| compute_tensorf_feat | Not yet run | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/FourierGrid_grid.py code served (permissive licence) · get_code("67fd5c6c899ca0af") |
| compute_tensorf_val | Not yet run | dvlab-research/LargeScaleNeRFPytorch/FourierGrid/FourierGrid_grid.py code served (permissive licence) · get_code("e6262a56833bb6d2") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Figure 1. Block-NeRF is a method that enables large-scale scene reconstruction by representing the environment using multiple compact NeRFs that each fit into memory. At inference time, Block-NeRF seamlessly combines renderings of the relevant NeRFs for the given area. In this example, we reconstruct the Alamo Square neighborhood in San Francisco using data collected over 3 months. Block-NeRF can update individual blocks of the environment without retraining on the entire scene, as demonstrated by the construction on the right. Video results can be found on the project website waymo.com/research/block-nerf.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2202.05263")
get_code_for_paper("2202.05263")
have("2202.05263")
Connect an agent — have() is free.