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synced 2026-11-11 02:24:38 +08:00
some edits
This commit is contained in:
Ethan Weber
parent
4acfce84c6
commit
ca6c8efbe2
@ -137,7 +137,7 @@ class NGPModel(Model):
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# metrics
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self.psnr = PeakSignalNoiseRatio(data_range=1.0)
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self.ssim = structural_similarity_index_measure
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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self.lpips = LearnedPerceptualImagePatchSimilarity(net_type="vgg")
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def get_training_callbacks(
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self, training_callback_attributes: TrainingCallbackAttributes
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@ -219,7 +219,7 @@ class NerfactoModel(Model):
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# metrics
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self.psnr = PeakSignalNoiseRatio(data_range=1.0)
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self.ssim = structural_similarity_index_measure
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self.lpips = LearnedPerceptualImagePatchSimilarity()
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self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
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def get_param_groups(self) -> Dict[str, List[Parameter]]:
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param_groups = {}
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@ -285,7 +285,7 @@ class WandbWriter(Writer):
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"""WandDB Writer Class"""
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def __init__(self, log_dir: Path, wandb_name: Optional[str] = None):
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wandb.init(project="nerfstudio-project", dir=str(log_dir), reinit=True)
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wandb.init(project="nerfstudio-project2", dir=str(log_dir), reinit=True)
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if wandb_name:
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wandb.run.name = wandb_name
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wandb.run.save()
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@ -13,6 +13,7 @@ import time
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import threading
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mipnerf360_capture_names = ["bicycle", "garden", "stump", "room", "counter", "kitchen", "bonsai"] # 7 splits
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# mipnerf360_capture_names = ["bicycle", "bonsai"] # 7 splits
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# 1/8 of input images used in the paper = 0.125 -> 1 - this = 0.875
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mipnerf360_table_rows = [
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# nerfacto method
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@ -22,11 +23,11 @@ mipnerf360_table_rows = [
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"--pipeline.eval_optimize_cameras True --pipeline.eval_optimize_appearance False --pipeline.datamanager.camera-optimizer.mode off --pipeline.model.use-appearance-embedding False nerfstudio-data --downscale-factor 4 --train-split-percentage 0.875",
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),
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# instant-ngp method
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(
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"instant-ngp-w/o-pose-app",
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"instant-ngp",
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"--pipeline.eval_optimize_cameras True --pipeline.eval_optimize_appearance False --pipeline.datamanager.camera-optimizer.mode off --pipeline.model.use-appearance-embedding False nerfstudio-data --downscale-factor 4 --train-split-percentage 0.875",
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),
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# (
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# "instant-ngp-w/o-pose-app",
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# "instant-ngp",
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# "--pipeline.eval_optimize_cameras True --pipeline.eval_optimize_appearance False --pipeline.datamanager.camera-optimizer.mode off --pipeline.model.use-appearance-embedding False nerfstudio-data --downscale-factor 4 --train-split-percentage 0.875",
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# ),
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]
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@ -109,7 +110,7 @@ def main(capture_names, table_rows, data_path: Path = Path("data/nerfstudio")):
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f"--data { data_path / capture_name}",
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"--output-dir outputs/nerfacto-ablations",
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"--trainer.steps-per-eval-batch 0 --trainer.steps-per-eval-image 0",
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"--trainer.steps-per-eval-all-images 5000 --trainer.max-num-iterations 30001",
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"--trainer.steps-per-eval-all-images 5000 --trainer.max-num-iterations 300001",
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f"--wandb-name {capture_name}_{table_row_name}",
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f"--experiment-name {capture_name}_{table_row_name}",
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table_row_command,
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@ -133,7 +134,7 @@ def main(capture_names, table_rows, data_path: Path = Path("data/nerfstudio")):
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def task():
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print("Starting command: ", command)
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# out = run_command(command, verbose=False)
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out = run_command(command, verbose=False)
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# time.sleep(5)
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print("Finished command: ", command)
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@ -152,6 +153,6 @@ def main(capture_names, table_rows, data_path: Path = Path("data/nerfstudio")):
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if __name__ == "__main__":
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pass
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# main(mipnerf360_capture_names, mipnerf360_table_rows, data_path=Path("data/nerfstudio-data-mipnerf360"))
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# pass
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main(mipnerf360_capture_names, mipnerf360_table_rows, data_path=Path("data/nerfstudio-data-mipnerf360"))
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# main(ablations_capture_names, ablations_table_rows)
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@ -12,34 +12,23 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"ename": "ImportError",
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"evalue": "cannot import name 'table_rows' from 'benchmark_nerfstudio_paper' (/home/ethanweber/nerfactory/scripts/benchmarking/benchmark_nerfstudio_paper.py)",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mImportError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn [4], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcopy\u001b[39;00m\n\u001b[1;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mpprint\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mbenchmark_nerfstudio_paper\u001b[39;00m \u001b[39mimport\u001b[39;00m table_rows\n\u001b[1;32m 6\u001b[0m \u001b[39m# methods sorted in the same order we want in the paper tables\u001b[39;00m\n\u001b[1;32m 7\u001b[0m methods \u001b[39m=\u001b[39m [row[\u001b[39m0\u001b[39m] \u001b[39mfor\u001b[39;00m row \u001b[39min\u001b[39;00m table_rows]\n",
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"\u001b[0;31mImportError\u001b[0m: cannot import name 'table_rows' from 'benchmark_nerfstudio_paper' (/home/ethanweber/nerfactory/scripts/benchmarking/benchmark_nerfstudio_paper.py)"
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]
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}
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],
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import copy\n",
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"import pprint\n",
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"from benchmark_nerfstudio_paper import table_rows\n",
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"from benchmark_nerfstudio_paper import ablations_table_rows\n",
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"from collections import defaultdict\n",
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"\n",
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"# methods sorted in the same order we want in the paper tables\n",
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"methods = [row[0] for row in table_rows]"
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"methods = [row[0] for row in ablations_table_rows]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 34,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -78,6 +67,8 @@
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" metric = f\"{metric:.2f}\"\n",
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" elif num_decimals == 3:\n",
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" metric = f\"{metric:.3f}\"\n",
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" else:\n",
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" metric = f\"{metric}\"\n",
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" results[capture_name][method] = metric\n",
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"\n",
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" # import pprint\n",
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@ -90,27 +81,83 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 44,
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"metadata": {},
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"outputs": [],
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"source": [
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"# the csv filenames\n",
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"# these are generated from exporting CSVs from wandb graphs\n",
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"# csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-psnr.csv\"\n",
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"# csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-ssim.csv\"\n",
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"# csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-lpips.csv\"\n",
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"csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-psnr.csv\"\n",
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"csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-ssim.csv\"\n",
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"csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-lpips.csv\"\n",
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"results_psrn = csv_filename_to_results(csv_filename_psrn, num_decimals=None)\n",
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"results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=None)\n",
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"results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=None)\n",
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"\n",
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"\n",
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"csv_filename_psrn = \"/home/ethanweber/nerfactory/wandb-jan9-psnr.csv\"\n",
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"csv_filename_ssim = \"/home/ethanweber/nerfactory/wandb-jan9-ssim.csv\"\n",
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"csv_filename_lpips = \"/home/ethanweber/nerfactory/wandb-jan9-lpips.csv\"\n",
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"results_psrn_2 = csv_filename_to_results(csv_filename_psrn, num_decimals=None)\n",
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"results_ssim_2 = csv_filename_to_results(csv_filename_ssim, num_decimals=None)\n",
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"results_lpips_2 = csv_filename_to_results(csv_filename_lpips, num_decimals=None)\n",
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"\n",
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"results_psrn = csv_filename_to_results(csv_filename_psrn)\n",
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"results_ssim = csv_filename_to_results(csv_filename_ssim, num_decimals=3)\n",
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"results_lpips = csv_filename_to_results(csv_filename_lpips, num_decimals=3)\n"
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"# merge the results\n",
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"for capture_name, results in results_psrn_2.items():\n",
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" results_psrn[capture_name].update(results)\n",
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"for capture_name, results in results_ssim_2.items():\n",
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" results_ssim[capture_name].update(results)\n",
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"for capture_name, results in results_lpips_2.items():\n",
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" results_lpips[capture_name].update(results)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 45,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_averages_from_results(results):\n",
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" averages = defaultdict(list)\n",
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" for capture_name, methods in results.items():\n",
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" for method, metric in methods.items():\n",
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" averages[method].append(float(metric))\n",
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" for method, metrics in averages.items():\n",
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" val = sum(metrics) / len(metrics)\n",
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" averages[method] = float(f\"{val:.4f}\")\n",
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" return averages"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 46,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"defaultdict(<class 'list'>,\n",
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" {'1-prop-network': 0.0668,\n",
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" 'l2-contraction': 0.0598,\n",
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" 'nerfacto': 0.0601,\n",
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" 'no-contraction': 0.1308,\n",
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" 'random-background-color': 0.0599,\n",
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" 'shared-prop-network': 0.0605,\n",
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" 'synthetic-on-real': 0.114,\n",
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" 'w/o-app': 0.0622,\n",
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" 'w/o-pose': 0.0614,\n",
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" 'w/o-pose-app': 0.0636})\n"
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]
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}
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],
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"source": [
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"pprint.pprint(get_averages_from_results(results_lpips))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -131,7 +178,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 48,
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"metadata": {},
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"outputs": [
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{
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@ -139,12 +186,28 @@
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"output_type": "stream",
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"text": [
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"['Egypt', 'person', 'kitchen', 'plane', 'dozer']\n",
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"no contraction & \\multicolumn{1}{r}{18.06 / 0.520 / 0.151} & \\multicolumn{1}{r}{21.96 / 0.511 / 0.125} & \\multicolumn{1}{r}{19.35 / 0.764 / 0.078} & \\multicolumn{1}{r}{18.45 / 0.519 / 0.161} & \\multicolumn{1}{r|}{19.53 / 0.786 / 0.081} \\\\ \n",
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"synthetic on real & \\multicolumn{1}{r}{18.51 / 0.546 / 0.119} & \\multicolumn{1}{r}{24.68 / 0.667 / 0.099} & \\multicolumn{1}{r}{20.98 / 0.745 / 0.074} & \\multicolumn{1}{r}{19.32 / 0.578 / 0.102} & \\multicolumn{1}{r|}{21.41 / 0.758 / 0.080} \\\\ \\hline\n",
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"nerfacto & \\multicolumn{1}{r}{22.714982986450195 / 0.8457087874412537 / 0.0387668460607528} & \\multicolumn{1}{r}{26.58124351501465 / 0.8588576912879944 / 0.0272847861051559} & \\multicolumn{1}{r}{21.02389335632324 / 0.8730602860450745 / 0.0538232773542404} & \\multicolumn{1}{r}{23.30121612548828 / 0.8020055890083313 / 0.0444588512182235} & \\multicolumn{1}{r|}{21.23827743530273 / 0.8753665089607239 / 0.0552346743643283} \\\\ \n",
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"w/o pose & \\multicolumn{1}{r}{22.709941864013672 / 0.8510463237762451 / 0.0384077467024326} & \\multicolumn{1}{r}{26.875864028930664 / 0.875615656375885 / 0.0267722699791193} & \\multicolumn{1}{r}{20.83899688720703 / 0.8627068996429443 / 0.0561660341918468} & \\multicolumn{1}{r}{23.130054473876957 / 0.8018836975097656 / 0.0470539852976799} & \\multicolumn{1}{r|}{21.35797691345215 / 0.8832465410232544 / 0.0533054806292057} \\\\ \n",
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"w/o app & \\multicolumn{1}{r}{23.50516891479492 / 0.8450330495834351 / 0.0371274687349796} & \\multicolumn{1}{r}{29.19672203063965 / 0.8823416829109192 / 0.0238535590469837} & \\multicolumn{1}{r}{23.997222900390625 / 0.8697791695594788 / 0.0548945926129817} & \\multicolumn{1}{r}{23.31549835205078 / 0.7850948572158813 / 0.0470350570976734} & \\multicolumn{1}{r|}{24.386877059936523 / 0.8825743198394775 / 0.0454414412379264} \\\\ \n",
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"w/o pose app & \\multicolumn{1}{r}{23.166139602661133 / 0.837310791015625 / 0.0366224125027656} & \\multicolumn{1}{r}{29.16166877746582 / 0.8803766369819641 / 0.023823982104659} & \\multicolumn{1}{r}{23.605825424194336 / 0.8618716597557068 / 0.0610234178602695} & \\multicolumn{1}{r}{22.98196029663086 / 0.7747009992599487 / 0.0486842803657054} & \\multicolumn{1}{r|}{24.1285343170166 / 0.8762974143028259 / 0.0484496317803859} \\\\ \n",
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"1 prop network & \\multicolumn{1}{r}{22.29441261291504 / 0.8302732706069946 / 0.0457336418330669} & \\multicolumn{1}{r}{26.79549789428711 / 0.8694913983345032 / 0.0297231022268533} & \\multicolumn{1}{r}{20.88402557373047 / 0.8669102787971497 / 0.0568834841251373} & \\multicolumn{1}{r}{23.38462257385254 / 0.8012978434562683 / 0.0477019511163234} & \\multicolumn{1}{r|}{21.26655387878418 / 0.8781195878982544 / 0.0575154237449169} \\\\ \n",
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"l2 contraction & \\multicolumn{1}{r}{22.735132217407227 / 0.8507159352302551 / 0.0394952110946178} & \\multicolumn{1}{r}{26.3044490814209 / 0.8442776203155518 / 0.0297974403947591} & \\multicolumn{1}{r}{20.831445693969727 / 0.870667040348053 / 0.0538116246461868} & \\multicolumn{1}{r}{23.17695617675781 / 0.8045668601989746 / 0.0425091050565242} & \\multicolumn{1}{r|}{21.17496681213379 / 0.8793404698371887 / 0.0562350191175937} \\\\ \n",
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"shared prop network & \\multicolumn{1}{r}{22.72172737121582 / 0.8462676405906677 / 0.036957062780857} & \\multicolumn{1}{r}{26.2338924407959 / 0.8363291621208191 / 0.030552914366126} & \\multicolumn{1}{r}{20.846466064453125 / 0.8672903180122375 / 0.0565056912600994} & \\multicolumn{1}{r}{23.31382369995117 / 0.8025220036506653 / 0.0451882556080818} & \\multicolumn{1}{r|}{21.209640502929688 / 0.8794581890106201 / 0.0534685812890529} \\\\ \n",
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"random background color & \\multicolumn{1}{r}{22.50684356689453 / 0.8338311910629272 / 0.0386323854327201} & \\multicolumn{1}{r}{26.413747787475582 / 0.8475838899612427 / 0.0302980989217758} & \\multicolumn{1}{r}{20.803401947021484 / 0.8670746684074402 / 0.0552690662443637} & \\multicolumn{1}{r}{23.181974411010746 / 0.7954545021057129 / 0.0450066737830638} & \\multicolumn{1}{r|}{21.30900382995605 / 0.8826950192451477 / 0.0531600639224052} \\\\ \n",
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"no contraction & \\multicolumn{1}{r}{18.0621395111084 / 0.5198838710784912 / 0.1507363319396972} & \\multicolumn{1}{r}{21.95572662353516 / 0.511165201663971 / 0.1250636577606201} & \\multicolumn{1}{r}{19.351024627685547 / 0.764460563659668 / 0.0784180983901023} & \\multicolumn{1}{r}{18.450420379638672 / 0.5193044543266296 / 0.1613750755786895} & \\multicolumn{1}{r|}{19.532262802124023 / 0.7856844067573547 / 0.0809869393706321} \\\\ \n",
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"synthetic on real & \\multicolumn{1}{r}{18.51077651977539 / 0.5463988184928894 / 0.1192879229784011} & \\multicolumn{1}{r}{24.6822566986084 / 0.6670649647712708 / 0.0987291559576988} & \\multicolumn{1}{r}{20.980388641357425 / 0.745147705078125 / 0.0741989538073539} & \\multicolumn{1}{r}{19.319589614868164 / 0.5777738690376282 / 0.1021366342902183} & \\multicolumn{1}{r|}{21.40807342529297 / 0.7580105066299438 / 0.0801835656166076} \\\\ \\hline\n",
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"\n",
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"['floating-tree', 'aspen', 'stump', 'sculpture', 'Giannini-Hall']\n",
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"no contraction & \\multicolumn{1}{r}{18.98 / 0.679 / 0.088} & \\multicolumn{1}{r}{15.80 / 0.330 / 0.342} & \\multicolumn{1}{r}{20.69 / 0.704 / 0.075} & \\multicolumn{1}{r}{21.43 / 0.720 / 0.073} & \\multicolumn{1}{r|}{17.75 / 0.596 / 0.134} \\\\ \n",
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"synthetic on real & \\multicolumn{1}{r}{21.13 / 0.726 / 0.072} & \\multicolumn{1}{r}{15.53 / 0.301 / 0.332} & \\multicolumn{1}{r}{23.86 / 0.786 / 0.053} & \\multicolumn{1}{r}{22.62 / 0.706 / 0.076} & \\multicolumn{1}{r|}{18.41 / 0.576 / 0.133} \\\\ \\hline\n"
|
||||
"nerfacto & \\multicolumn{1}{r}{20.39972686767578 / 0.854109525680542 / 0.0524107255041599} & \\multicolumn{1}{r}{18.57237243652344 / 0.6354746222496033 / 0.1751366555690765} & \\multicolumn{1}{r}{21.928449630737305 / 0.7976410388946533 / 0.0387384481728076} & \\multicolumn{1}{r}{22.58584213256836 / 0.7889150381088257 / 0.0407779291272163} & \\multicolumn{1}{r|}{19.99639129638672 / 0.7731102108955383 / 0.0745075196027755} \\\\ \n",
|
||||
"w/o pose & \\multicolumn{1}{r}{20.298728942871094 / 0.8402881622314453 / 0.0546704977750778} & \\multicolumn{1}{r}{18.61675453186035 / 0.6382865905761719 / 0.1753666847944259} & \\multicolumn{1}{r}{21.633838653564453 / 0.7653179168701172 / 0.0428420193493366} & \\multicolumn{1}{r}{22.506526947021484 / 0.7845871448516846 / 0.0455470941960811} & \\multicolumn{1}{r|}{19.97289276123047 / 0.7722745537757874 / 0.0739260166883468} \\\\ \n",
|
||||
"w/o app & \\multicolumn{1}{r}{22.46498680114746 / 0.8535845279693604 / 0.0616657771170139} & \\multicolumn{1}{r}{18.27329635620117 / 0.6129483580589294 / 0.1905485093593597} & \\multicolumn{1}{r}{25.68264579772949 / 0.8736064434051514 / 0.0317401029169559} & \\multicolumn{1}{r}{23.268043518066406 / 0.7738019227981567 / 0.0487339384853839} & \\multicolumn{1}{r|}{21.44996833801269 / 0.7652416825294495 / 0.0814294144511222} \\\\ \n",
|
||||
"w/o pose app & \\multicolumn{1}{r}{22.154300689697266 / 0.8340064287185669 / 0.0661337822675705} & \\multicolumn{1}{r}{18.28131675720215 / 0.611172080039978 / 0.1861454993486404} & \\multicolumn{1}{r}{25.562631607055664 / 0.8677808046340942 / 0.0340047031641006} & \\multicolumn{1}{r}{22.98941802978516 / 0.76247638463974 / 0.0532486699521541} & \\multicolumn{1}{r|}{21.344554901123047 / 0.7619099020957947 / 0.0781947895884513} \\\\ \n",
|
||||
"1 prop network & \\multicolumn{1}{r}{20.28233528137207 / 0.8498139977455139 / 0.0570690296590328} & \\multicolumn{1}{r}{18.41587829589844 / 0.6070924401283264 / 0.2094678878784179} & \\multicolumn{1}{r}{22.85092544555664 / 0.8734478950500488 / 0.0329417251050472} & \\multicolumn{1}{r}{22.60772132873535 / 0.8046638369560242 / 0.0431603491306304} & \\multicolumn{1}{r|}{19.89984703063965 / 0.759652853012085 / 0.0880114957690239} \\\\ \n",
|
||||
"l2 contraction & \\multicolumn{1}{r}{20.49176979064941 / 0.8501678109169006 / 0.0527788065373897} & \\multicolumn{1}{r}{18.66337013244629 / 0.6445528268814087 / 0.1680227816104889} & \\multicolumn{1}{r}{22.26547622680664 / 0.8150904178619385 / 0.0378103740513324} & \\multicolumn{1}{r}{22.462072372436523 / 0.7815755009651184 / 0.0426461324095726} & \\multicolumn{1}{r|}{19.985918045043945 / 0.7727018594741821 / 0.0751706212759018} \\\\ \n",
|
||||
"shared prop network & \\multicolumn{1}{r}{20.333086013793945 / 0.8504053950309753 / 0.0537901557981967} & \\multicolumn{1}{r}{18.6508731842041 / 0.6390713453292847 / 0.1719826906919479} & \\multicolumn{1}{r}{22.207321166992188 / 0.805411696434021 / 0.0403469167649745} & \\multicolumn{1}{r}{22.50098991394043 / 0.7922176718711853 / 0.0416620261967182} & \\multicolumn{1}{r|}{19.98356437683105 / 0.7727880477905273 / 0.0745216161012649} \\\\ \n",
|
||||
"random background color & \\multicolumn{1}{r}{20.243900299072266 / 0.8465315699577332 / 0.0529390051960945} & \\multicolumn{1}{r}{18.56953430175781 / 0.636640191078186 / 0.1718863695859909} & \\multicolumn{1}{r}{22.578887939453125 / 0.8352341055870056 / 0.0351601541042327} & \\multicolumn{1}{r}{22.478111267089844 / 0.7781183123588562 / 0.0417613536119461} & \\multicolumn{1}{r|}{20.105976104736328 / 0.7749199867248535 / 0.0752039030194282} \\\\ \n",
|
||||
"no contraction & \\multicolumn{1}{r}{18.98202896118164 / 0.6787521839141846 / 0.0879055261611938} & \\multicolumn{1}{r}{15.803391456604004 / 0.3301353752613067 / 0.3417288959026336} & \\multicolumn{1}{r}{20.686363220214844 / 0.7041175365447998 / 0.0748037695884704} & \\multicolumn{1}{r}{21.432249069213867 / 0.7197808623313904 / 0.0730623304843902} & \\multicolumn{1}{r|}{17.751110076904297 / 0.5955697298049927 / 0.1335323601961136} \\\\ \n",
|
||||
"synthetic on real & \\multicolumn{1}{r}{21.130878448486328 / 0.7259055376052856 / 0.0717189684510231} & \\multicolumn{1}{r}{15.527592658996582 / 0.3011459112167358 / 0.3317984342575073} & \\multicolumn{1}{r}{23.858999252319336 / 0.7855907678604126 / 0.0529582537710666} & \\multicolumn{1}{r}{22.620250701904297 / 0.7056115865707397 / 0.0756969302892685} & \\multicolumn{1}{r|}{18.407384872436523 / 0.5756094455718994 / 0.1331061422824859} \\\\ \\hline\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -186,6 +249,12 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# mipnerf360 experiments\n",
|
||||
"\n",
|
||||
"csv_filename_psrn = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-psnr.csv\"\n",
|
||||
"csv_filename_ssim = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-ssim.csv\"\n",
|
||||
"csv_filename_lpips = \"/home/ethanweber/nerfactory/mipnerf360-nerfacto-lpips.csv\"\n",
|
||||
"\n",
|
||||
"csv_filename_psrn = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-psnr.csv\"\n",
|
||||
"csv_filename_ssim = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-ssim.csv\"\n",
|
||||
"csv_filename_lpips = \"/home/ethanweber/nerfactory/mipnerf360-instant-ngp-lpips.csv\"\n",
|
||||
|
||||
9566
scripts/benchmarking/nerfstudio-Egypt-camerapath.json
Normal file
9566
scripts/benchmarking/nerfstudio-Egypt-camerapath.json
Normal file
File diff suppressed because it is too large
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Reference in New Issue
Block a user