GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation
Run this model in Node.js with one line of code:
npm install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import Replicate from "replicate";
const replicate = new Replicate({
auth: process.env.REPLICATE_API_TOKEN,
});Run camenduru/grm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run(
"camenduru/grm:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
{
input: {
seed: 21,
model: "Zero123++ v1.2",
fuse_mesh: true,
input_image: "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
}
);
// To access the file URL:
console.log(output[0].url()); //=> "http://example.com"
// To write the file to disk:
fs.writeFile("my-image.png", output[0]);To learn more, take a look at the guide on getting started with Node.js.
pip install replicateREPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
import replicateRun camenduru/grm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run(
"camenduru/grm:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
input={
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": True,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
)
print(output)To learn more, take a look at the guide on getting started with Python.
REPLICATE_API_TOKEN environment variable:export REPLICATE_API_TOKEN=<paste-your-token-here>Find your API token in your account settings.
Run camenduru/grm using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
-H "Content-Type: application/json" \
-H "Prefer: wait" \
-d $'{
"version": "8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0",
"input": {
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": true,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
}
}' \
https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
brew install cogIf you don’t have Homebrew, there are other installation options available.
Run this to download the model and run it in your local environment:
cog predict r8.im/camenduru/grm@sha256:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0 \
-i 'seed=21' \
-i 'model="Zero123++ v1.2"' \
-i 'fuse_mesh=true' \
-i 'input_image="https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"'To learn more, take a look at the Cog documentation.
Run this to download the model and run it in your local environment:
docker run -d -p 5000:5000 r8.im/camenduru/grm@sha256:8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0curl -s -X POST \ -H "Content-Type: application/json" \ -d $'{ "input": { "seed": 21, "model": "Zero123++ v1.2", "fuse_mesh": true, "input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png" } }' \ http://localhost:5000/predictions
To learn more, take a look at the Cog documentation.
Add a payment method to run this model.
Each run costs approximately $0.41. Alternatively, try out our featured models for free.
By signing in, you agree to our
terms of service and privacy policy
{
"completed_at": "2024-03-30T16:52:11.872009Z",
"created_at": "2024-03-30T16:51:45.995453Z",
"data_removed": false,
"error": null,
"id": "rgfiruzbto74d7mhkfq6llnfwm",
"input": {
"seed": 21,
"model": "Zero123++ v1.2",
"fuse_mesh": true,
"input_image": "https://replicate.delivery/pbxt/Kf2I8ezAPJ9a6YZJUnDkoGq7urlPtjrA5hRS02D0knxS2KrW/dragon2.png"
},
"logs": "Running image-to-3d with seed 21...\n 0%| | 0/30 [00:00<?, ?it/s]\n 7%|▋ | 2/30 [00:00<00:02, 9.99it/s]\n 13%|█▎ | 4/30 [00:00<00:02, 10.24it/s]\n 20%|██ | 6/30 [00:00<00:02, 9.76it/s]\n 27%|██▋ | 8/30 [00:00<00:02, 10.08it/s]\n 33%|███▎ | 10/30 [00:00<00:01, 10.21it/s]\n 40%|████ | 12/30 [00:01<00:01, 10.23it/s]\n 47%|████▋ | 14/30 [00:01<00:01, 10.30it/s]\n 53%|█████▎ | 16/30 [00:01<00:01, 10.34it/s]\n 60%|██████ | 18/30 [00:01<00:01, 10.03it/s]\n 67%|██████▋ | 20/30 [00:01<00:00, 10.11it/s]\n 73%|███████▎ | 22/30 [00:02<00:00, 10.19it/s]\n 80%|████████ | 24/30 [00:02<00:00, 10.20it/s]\n 87%|████████▋ | 26/30 [00:02<00:00, 10.26it/s]\n 93%|█████████▎| 28/30 [00:02<00:00, 9.94it/s]\n100%|██████████| 30/30 [00:02<00:00, 10.01it/s]\n100%|██████████| 30/30 [00:02<00:00, 10.11it/s]\nIntegrate images into the TSDF volume.\n 0%| | 0/200 [00:00<?, ?it/s]\n 0%| | 1/200 [00:00<00:26, 7.58it/s]\n 2%|▏ | 4/200 [00:00<00:10, 18.13it/s]\n 4%|▍ | 9/200 [00:00<00:06, 29.80it/s]\n 6%|▋ | 13/200 [00:00<00:07, 24.91it/s]\n 8%|▊ | 16/200 [00:00<00:07, 25.94it/s]\n 10%|▉ | 19/200 [00:00<00:08, 21.92it/s]\n 11%|█ | 22/200 [00:01<00:08, 19.84it/s]\n 12%|█▎ | 25/200 [00:01<00:08, 20.77it/s]\n 15%|█▌ | 30/200 [00:01<00:07, 23.61it/s]\n 16%|█▋ | 33/200 [00:01<00:07, 23.33it/s]\n 18%|█▊ | 36/200 [00:01<00:06, 24.62it/s]\n 20%|█▉ | 39/200 [00:01<00:06, 25.79it/s]\n 21%|██ | 42/200 [00:01<00:07, 22.56it/s]\n 22%|██▎ | 45/200 [00:01<00:06, 22.93it/s]\n 24%|██▍ | 49/200 [00:02<00:06, 23.51it/s]\n 26%|██▌ | 52/200 [00:02<00:06, 24.18it/s]\n 28%|██▊ | 55/200 [00:02<00:05, 24.87it/s]\n 29%|██▉ | 58/200 [00:02<00:05, 25.32it/s]\n 31%|███ | 62/200 [00:02<00:05, 25.29it/s]\n 32%|███▎ | 65/200 [00:02<00:05, 25.24it/s]\n 34%|███▍ | 68/200 [00:02<00:05, 25.60it/s]\n 36%|███▌ | 72/200 [00:02<00:04, 28.64it/s]\n 38%|███▊ | 76/200 [00:03<00:04, 27.01it/s]\n 40%|███▉ | 79/200 [00:03<00:04, 26.65it/s]\n 41%|████ | 82/200 [00:03<00:05, 23.33it/s]\n 42%|████▎ | 85/200 [00:03<00:04, 24.28it/s]\n 44%|████▍ | 88/200 [00:03<00:04, 24.93it/s]\n 46%|████▌ | 92/200 [00:03<00:03, 28.13it/s]\n 48%|████▊ | 96/200 [00:03<00:03, 30.36it/s]\n 50%|█████ | 100/200 [00:04<00:03, 27.14it/s]\n 52%|█████▏ | 104/200 [00:04<00:03, 29.18it/s]\n 54%|█████▍ | 108/200 [00:04<00:03, 26.40it/s]\n 56%|█████▌ | 111/200 [00:04<00:03, 26.15it/s]\n 57%|█████▋ | 114/200 [00:04<00:03, 26.57it/s]\n 59%|█████▉ | 118/200 [00:04<00:03, 26.03it/s]\n 60%|██████ | 121/200 [00:04<00:03, 26.25it/s]\n 62%|██████▎ | 125/200 [00:04<00:02, 28.84it/s]\n 64%|██████▍ | 129/200 [00:05<00:02, 31.05it/s]\n 66%|██████▋ | 133/200 [00:05<00:02, 27.33it/s]\n 68%|██████▊ | 136/200 [00:05<00:02, 27.21it/s]\n 70%|███████ | 140/200 [00:05<00:02, 29.15it/s]\n 72%|███████▏ | 143/200 [00:05<00:02, 25.60it/s]\n 73%|███████▎ | 146/200 [00:05<00:02, 25.64it/s]\n 75%|███████▌ | 150/200 [00:05<00:01, 28.53it/s]\n 76%|███████▋ | 153/200 [00:05<00:01, 28.36it/s]\n 78%|███████▊ | 156/200 [00:06<00:01, 24.70it/s]\n 80%|████████ | 160/200 [00:06<00:01, 27.08it/s]\n 82%|████████▏ | 163/200 [00:06<00:01, 26.99it/s]\n 83%|████████▎ | 166/200 [00:06<00:01, 27.05it/s]\n 84%|████████▍ | 169/200 [00:06<00:01, 24.02it/s]\n 87%|████████▋ | 174/200 [00:06<00:00, 29.33it/s]\n 89%|████████▉ | 178/200 [00:06<00:00, 26.24it/s]\n 91%|█████████ | 182/200 [00:07<00:00, 28.56it/s]\n 94%|█████████▎| 187/200 [00:07<00:00, 32.72it/s]\n 96%|█████████▌| 191/200 [00:07<00:00, 29.52it/s]\n 98%|█████████▊| 196/200 [00:07<00:00, 33.36it/s]\n100%|██████████| 200/200 [00:07<00:00, 26.59it/s]\nExtract a triangle mesh from the volume and export it.\n[Open3D DEBUG] [ClusterConnectedTriangles] Compute triangle adjacency\n[Open3D DEBUG] [ClusterConnectedTriangles] Done computing triangle adjacency\n[Open3D DEBUG] [ClusterConnectedTriangles] Done clustering, #clusters=46060\n[Mesh loading] v: torch.Size([70902, 3]), f: torch.Size([141445, 3])\n[Mesh loading] vn: torch.Size([70902, 3]), fn: torch.Size([141445, 3])\ntensor(0.2557, device='cuda:0') tensor(0.7722, device='cuda:0')",
"metrics": {
"predict_time": 25.860297,
"total_time": 25.876556
},
"output": [
"https://replicate.delivery/pbxt/cX5JxYRwl1aMLdHzrhJQXrUwOOlIQMzoU5llxBr9mI4O1XpE/gs.mp4",
"https://replicate.delivery/pbxt/JkBYe7jHeMgezI0IP4ycCOjCaSw80EeHFLJUGuwonBbsT9VKB/mesh.glb",
"https://replicate.delivery/pbxt/hXZRhRPj81YuAhRzyteGb6vkmBk8fGy2G23G3h02oTb7UfKlA/gs_vis.ply",
"https://replicate.delivery/pbxt/0dVVyRrdrfVzfUzMEIcpuqjeJjYnJPwxsmF72AzwEqg2peVKB/gs.ply"
],
"started_at": "2024-03-30T16:51:46.011712Z",
"status": "succeeded",
"urls": {
"get": "https://api.replicate.com/v1/predictions/rgfiruzbto74d7mhkfq6llnfwm",
"cancel": "https://api.replicate.com/v1/predictions/rgfiruzbto74d7mhkfq6llnfwm/cancel"
},
"version": "8c489b23ddc6a9b0484f8b6f55f9b24fd792a84a6715d02f7e3ec63325bae4f0"
}Running image-to-3d with seed 21...
0%| | 0/30 [00:00<?, ?it/s]
7%|▋ | 2/30 [00:00<00:02, 9.99it/s]
13%|█▎ | 4/30 [00:00<00:02, 10.24it/s]
20%|██ | 6/30 [00:00<00:02, 9.76it/s]
27%|██▋ | 8/30 [00:00<00:02, 10.08it/s]
33%|███▎ | 10/30 [00:00<00:01, 10.21it/s]
40%|████ | 12/30 [00:01<00:01, 10.23it/s]
47%|████▋ | 14/30 [00:01<00:01, 10.30it/s]
53%|█████▎ | 16/30 [00:01<00:01, 10.34it/s]
60%|██████ | 18/30 [00:01<00:01, 10.03it/s]
67%|██████▋ | 20/30 [00:01<00:00, 10.11it/s]
73%|███████▎ | 22/30 [00:02<00:00, 10.19it/s]
80%|████████ | 24/30 [00:02<00:00, 10.20it/s]
87%|████████▋ | 26/30 [00:02<00:00, 10.26it/s]
93%|█████████▎| 28/30 [00:02<00:00, 9.94it/s]
100%|██████████| 30/30 [00:02<00:00, 10.01it/s]
100%|██████████| 30/30 [00:02<00:00, 10.11it/s]
Integrate images into the TSDF volume.
0%| | 0/200 [00:00<?, ?it/s]
0%| | 1/200 [00:00<00:26, 7.58it/s]
2%|▏ | 4/200 [00:00<00:10, 18.13it/s]
4%|▍ | 9/200 [00:00<00:06, 29.80it/s]
6%|▋ | 13/200 [00:00<00:07, 24.91it/s]
8%|▊ | 16/200 [00:00<00:07, 25.94it/s]
10%|▉ | 19/200 [00:00<00:08, 21.92it/s]
11%|█ | 22/200 [00:01<00:08, 19.84it/s]
12%|█▎ | 25/200 [00:01<00:08, 20.77it/s]
15%|█▌ | 30/200 [00:01<00:07, 23.61it/s]
16%|█▋ | 33/200 [00:01<00:07, 23.33it/s]
18%|█▊ | 36/200 [00:01<00:06, 24.62it/s]
20%|█▉ | 39/200 [00:01<00:06, 25.79it/s]
21%|██ | 42/200 [00:01<00:07, 22.56it/s]
22%|██▎ | 45/200 [00:01<00:06, 22.93it/s]
24%|██▍ | 49/200 [00:02<00:06, 23.51it/s]
26%|██▌ | 52/200 [00:02<00:06, 24.18it/s]
28%|██▊ | 55/200 [00:02<00:05, 24.87it/s]
29%|██▉ | 58/200 [00:02<00:05, 25.32it/s]
31%|███ | 62/200 [00:02<00:05, 25.29it/s]
32%|███▎ | 65/200 [00:02<00:05, 25.24it/s]
34%|███▍ | 68/200 [00:02<00:05, 25.60it/s]
36%|███▌ | 72/200 [00:02<00:04, 28.64it/s]
38%|███▊ | 76/200 [00:03<00:04, 27.01it/s]
40%|███▉ | 79/200 [00:03<00:04, 26.65it/s]
41%|████ | 82/200 [00:03<00:05, 23.33it/s]
42%|████▎ | 85/200 [00:03<00:04, 24.28it/s]
44%|████▍ | 88/200 [00:03<00:04, 24.93it/s]
46%|████▌ | 92/200 [00:03<00:03, 28.13it/s]
48%|████▊ | 96/200 [00:03<00:03, 30.36it/s]
50%|█████ | 100/200 [00:04<00:03, 27.14it/s]
52%|█████▏ | 104/200 [00:04<00:03, 29.18it/s]
54%|█████▍ | 108/200 [00:04<00:03, 26.40it/s]
56%|█████▌ | 111/200 [00:04<00:03, 26.15it/s]
57%|█████▋ | 114/200 [00:04<00:03, 26.57it/s]
59%|█████▉ | 118/200 [00:04<00:03, 26.03it/s]
60%|██████ | 121/200 [00:04<00:03, 26.25it/s]
62%|██████▎ | 125/200 [00:04<00:02, 28.84it/s]
64%|██████▍ | 129/200 [00:05<00:02, 31.05it/s]
66%|██████▋ | 133/200 [00:05<00:02, 27.33it/s]
68%|██████▊ | 136/200 [00:05<00:02, 27.21it/s]
70%|███████ | 140/200 [00:05<00:02, 29.15it/s]
72%|███████▏ | 143/200 [00:05<00:02, 25.60it/s]
73%|███████▎ | 146/200 [00:05<00:02, 25.64it/s]
75%|███████▌ | 150/200 [00:05<00:01, 28.53it/s]
76%|███████▋ | 153/200 [00:05<00:01, 28.36it/s]
78%|███████▊ | 156/200 [00:06<00:01, 24.70it/s]
80%|████████ | 160/200 [00:06<00:01, 27.08it/s]
82%|████████▏ | 163/200 [00:06<00:01, 26.99it/s]
83%|████████▎ | 166/200 [00:06<00:01, 27.05it/s]
84%|████████▍ | 169/200 [00:06<00:01, 24.02it/s]
87%|████████▋ | 174/200 [00:06<00:00, 29.33it/s]
89%|████████▉ | 178/200 [00:06<00:00, 26.24it/s]
91%|█████████ | 182/200 [00:07<00:00, 28.56it/s]
94%|█████████▎| 187/200 [00:07<00:00, 32.72it/s]
96%|█████████▌| 191/200 [00:07<00:00, 29.52it/s]
98%|█████████▊| 196/200 [00:07<00:00, 33.36it/s]
100%|██████████| 200/200 [00:07<00:00, 26.59it/s]
Extract a triangle mesh from the volume and export it.
[Open3D DEBUG] [ClusterConnectedTriangles] Compute triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done computing triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done clustering, #clusters=46060
[Mesh loading] v: torch.Size([70902, 3]), f: torch.Size([141445, 3])
[Mesh loading] vn: torch.Size([70902, 3]), fn: torch.Size([141445, 3])
tensor(0.2557, device='cuda:0') tensor(0.7722, device='cuda:0')This model costs approximately $0.41 to run on Replicate, or 2 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.
This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 5 minutes.
🐣 Please follow me for new updates https://twitter.com/camenduru
🔥 Please join our discord server https://discord.gg/k5BwmmvJJU
🥳 Please join my patreon community https://patreon.com/camenduru
https://replicate.com/camenduru/grm
https://github.com/justimyhxu/GRM
https://arxiv.org/abs/2403.14621
https://justimyhxu.github.io/projects/grm/
This model is cold. You'll get a fast response if the model is warm and already running, and a slower response if the model is cold and starting up.
Choose a file from your machine
Hint: you can also drag files onto the input
Running image-to-3d with seed 21...
0%| | 0/30 [00:00<?, ?it/s]
7%|▋ | 2/30 [00:00<00:02, 9.99it/s]
13%|█▎ | 4/30 [00:00<00:02, 10.24it/s]
20%|██ | 6/30 [00:00<00:02, 9.76it/s]
27%|██▋ | 8/30 [00:00<00:02, 10.08it/s]
33%|███▎ | 10/30 [00:00<00:01, 10.21it/s]
40%|████ | 12/30 [00:01<00:01, 10.23it/s]
47%|████▋ | 14/30 [00:01<00:01, 10.30it/s]
53%|█████▎ | 16/30 [00:01<00:01, 10.34it/s]
60%|██████ | 18/30 [00:01<00:01, 10.03it/s]
67%|██████▋ | 20/30 [00:01<00:00, 10.11it/s]
73%|███████▎ | 22/30 [00:02<00:00, 10.19it/s]
80%|████████ | 24/30 [00:02<00:00, 10.20it/s]
87%|████████▋ | 26/30 [00:02<00:00, 10.26it/s]
93%|█████████▎| 28/30 [00:02<00:00, 9.94it/s]
100%|██████████| 30/30 [00:02<00:00, 10.01it/s]
100%|██████████| 30/30 [00:02<00:00, 10.11it/s]
Integrate images into the TSDF volume.
0%| | 0/200 [00:00<?, ?it/s]
0%| | 1/200 [00:00<00:26, 7.58it/s]
2%|▏ | 4/200 [00:00<00:10, 18.13it/s]
4%|▍ | 9/200 [00:00<00:06, 29.80it/s]
6%|▋ | 13/200 [00:00<00:07, 24.91it/s]
8%|▊ | 16/200 [00:00<00:07, 25.94it/s]
10%|▉ | 19/200 [00:00<00:08, 21.92it/s]
11%|█ | 22/200 [00:01<00:08, 19.84it/s]
12%|█▎ | 25/200 [00:01<00:08, 20.77it/s]
15%|█▌ | 30/200 [00:01<00:07, 23.61it/s]
16%|█▋ | 33/200 [00:01<00:07, 23.33it/s]
18%|█▊ | 36/200 [00:01<00:06, 24.62it/s]
20%|█▉ | 39/200 [00:01<00:06, 25.79it/s]
21%|██ | 42/200 [00:01<00:07, 22.56it/s]
22%|██▎ | 45/200 [00:01<00:06, 22.93it/s]
24%|██▍ | 49/200 [00:02<00:06, 23.51it/s]
26%|██▌ | 52/200 [00:02<00:06, 24.18it/s]
28%|██▊ | 55/200 [00:02<00:05, 24.87it/s]
29%|██▉ | 58/200 [00:02<00:05, 25.32it/s]
31%|███ | 62/200 [00:02<00:05, 25.29it/s]
32%|███▎ | 65/200 [00:02<00:05, 25.24it/s]
34%|███▍ | 68/200 [00:02<00:05, 25.60it/s]
36%|███▌ | 72/200 [00:02<00:04, 28.64it/s]
38%|███▊ | 76/200 [00:03<00:04, 27.01it/s]
40%|███▉ | 79/200 [00:03<00:04, 26.65it/s]
41%|████ | 82/200 [00:03<00:05, 23.33it/s]
42%|████▎ | 85/200 [00:03<00:04, 24.28it/s]
44%|████▍ | 88/200 [00:03<00:04, 24.93it/s]
46%|████▌ | 92/200 [00:03<00:03, 28.13it/s]
48%|████▊ | 96/200 [00:03<00:03, 30.36it/s]
50%|█████ | 100/200 [00:04<00:03, 27.14it/s]
52%|█████▏ | 104/200 [00:04<00:03, 29.18it/s]
54%|█████▍ | 108/200 [00:04<00:03, 26.40it/s]
56%|█████▌ | 111/200 [00:04<00:03, 26.15it/s]
57%|█████▋ | 114/200 [00:04<00:03, 26.57it/s]
59%|█████▉ | 118/200 [00:04<00:03, 26.03it/s]
60%|██████ | 121/200 [00:04<00:03, 26.25it/s]
62%|██████▎ | 125/200 [00:04<00:02, 28.84it/s]
64%|██████▍ | 129/200 [00:05<00:02, 31.05it/s]
66%|██████▋ | 133/200 [00:05<00:02, 27.33it/s]
68%|██████▊ | 136/200 [00:05<00:02, 27.21it/s]
70%|███████ | 140/200 [00:05<00:02, 29.15it/s]
72%|███████▏ | 143/200 [00:05<00:02, 25.60it/s]
73%|███████▎ | 146/200 [00:05<00:02, 25.64it/s]
75%|███████▌ | 150/200 [00:05<00:01, 28.53it/s]
76%|███████▋ | 153/200 [00:05<00:01, 28.36it/s]
78%|███████▊ | 156/200 [00:06<00:01, 24.70it/s]
80%|████████ | 160/200 [00:06<00:01, 27.08it/s]
82%|████████▏ | 163/200 [00:06<00:01, 26.99it/s]
83%|████████▎ | 166/200 [00:06<00:01, 27.05it/s]
84%|████████▍ | 169/200 [00:06<00:01, 24.02it/s]
87%|████████▋ | 174/200 [00:06<00:00, 29.33it/s]
89%|████████▉ | 178/200 [00:06<00:00, 26.24it/s]
91%|█████████ | 182/200 [00:07<00:00, 28.56it/s]
94%|█████████▎| 187/200 [00:07<00:00, 32.72it/s]
96%|█████████▌| 191/200 [00:07<00:00, 29.52it/s]
98%|█████████▊| 196/200 [00:07<00:00, 33.36it/s]
100%|██████████| 200/200 [00:07<00:00, 26.59it/s]
Extract a triangle mesh from the volume and export it.
[Open3D DEBUG] [ClusterConnectedTriangles] Compute triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done computing triangle adjacency
[Open3D DEBUG] [ClusterConnectedTriangles] Done clustering, #clusters=46060
[Mesh loading] v: torch.Size([70902, 3]), f: torch.Size([141445, 3])
[Mesh loading] vn: torch.Size([70902, 3]), fn: torch.Size([141445, 3])
tensor(0.2557, device='cuda:0') tensor(0.7722, device='cuda:0')