ReconViaGen 0.5
集成
模型 id
reconviagen-0.5
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/reconviagen-0.5?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/reconviagen-0.5?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/reconviagen-0.5", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
# 1. Connect once (Claude Code; Cursor, VS Code, Claude Desktop: https://doitong.com/zh/developers/docs/mcp)
claude mcp add --transport http doitong-models https://api.doitong.com/v1/mcp \
--header "Authorization: Bearer $DOITONG_API_KEY"
# 2. Your agent calls the "run" tool with:
{
"model_id": "reconviagen-0.5",
"input": {
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}
}
通过 Doitong API 调用 ReconViaGen 0.5 模型(reconviagen-0.5)。智能体文档:https://doitong.com/zh/developers/models/reconviagen-0.5.md
POST https://api.doitong.com/v1/run/reconviagen-0.5?wait=20
Authorization: Bearer $DOITONG_API_KEY
Content-Type: application/json
{
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}
如果响应状态为 "queued" 或 "running",请轮询 GET https://api.doitong.com/v1/requests/REQUEST_ID?wait=20,直到状态变为 "succeeded";文件位于 outputs[].url。使用相同请求体 POST https://api.doitong.com/v1/quote/reconviagen-0.5,可在运行前返回确切价格。
此请求: $0.30 300 API 积分 在此页面运行
你的新 API 密钥。 它仅在此时显示,并且已填入下方的代码和 API 密钥字段中。请妥善保密:切勿将其放入在浏览器中运行的代码。
构建请求
控件根据模型架构生成。更改控件时,代码和价格会同步更新。
POST
/run/reconviagen-0.5?wait=20
报价 → $0.30
# Run the model; wait up to 20 s for the finished result in the same call
curl -X POST "https://api.doitong.com/v1/run/reconviagen-0.5?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}'
# Still "queued" or "running"? Poll with the id from the response:
curl "https://api.doitong.com/v1/requests/REQUEST_ID?wait=20" \
-H "Authorization: Bearer $DOITONG_API_KEY"
const API = "https://api.doitong.com/v1";
const headers = {
"Authorization": "Bearer " + process.env.DOITONG_API_KEY,
"Content-Type": "application/json",
};
// Run the model; wait up to 20 s for the finished result in the same call
let res = await fetch(API + "/run/reconviagen-0.5?wait=20", {
method: "POST",
headers,
body: JSON.stringify({
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
}),
});
let request = await res.json();
if (!res.ok) throw new Error(request.error.code + ": " + request.error.message);
// Not finished yet: long-poll until it is
while (request.status === "queued" || request.status === "running") {
res = await fetch(API + "/requests/" + request.id + "?wait=20", { headers });
request = await res.json();
}
if (request.status !== "succeeded") throw new Error(JSON.stringify(request.error));
console.log(request.outputs.map((o) => o.url), request.cost.credits + " API credits ($" + request.cost.usd + ")");
import os
import requests
API = "https://api.doitong.com/v1"
headers = {"Authorization": "Bearer " + os.environ["DOITONG_API_KEY"]}
payload = {
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg",
],
}
# Run the model; wait up to 20 s for the finished result in the same call
r = requests.post(API + "/run/reconviagen-0.5", params={"wait": 20}, json=payload, headers=headers)
request = r.json()
if not r.ok:
raise RuntimeError(request["error"]["code"] + ": " + request["error"]["message"])
# Not finished yet: long-poll until it is
while request["status"] in ("queued", "running"):
request = requests.get(API + "/requests/" + request["id"], params={"wait": 20}, headers=headers).json()
if request["status"] != "succeeded":
raise RuntimeError(request["error"])
print([o["url"] for o in request["outputs"]], request["cost"]["credits"], "API credits", "$" + request["cost"]["usd"])
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "run",
"arguments": {
"model_id": "reconviagen-0.5",
"input": {
"images": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
},
"wait_seconds": 20
}
}
}
关于 ReconViaGen 0.5
根据 `image_urls` 构建 3D 模型,`image_urls` 接收同一物体的 1 到 16 个视角;视角越多,重建效果越好。返回带 PBR 材质的 GLB 文件。`resolution` 可以是 512、1024 或 1536,`texture_size` 可以是 1024、2048 或 4096,`decimation_target` 将网格顶点数设置为 5,000 到 2,000,000。`ss_source` 和 `multi_image_strategy` 选择结构和视角的组合方式,并提供分阶段的引导和采样控制。
参数
发送规范名称或原生名称之一,不要同时发送两者。 规范名称
名称类型
默认值允许值
描述
seed可选
类型integer | null
用于可复现随机生成的种子。
resolution可选
类型enum
默认值1024
51210241536
输出分辨率;提高分辨率会增加细节,但耗时更长。
ss_source可选
类型enum
默认值mesh
directmeshmvtrellis2
用于构建稀疏结构的来源。选择 'direct' 可获得最高速度,选择 'mesh' 可获得最高质量,或选择 'mvtrellis2' 使用 TRELLIS.2 进行多视图处理。
ss_guidance_strength可选
类型number
默认值7.5
0 – 10
控制初始 3D 结构对输入的保真度;该值越高,可能噪声越大,但匹配度越高。
ss_guidance_rescale可选
类型number
默认值0.7
0 – 1
减少第 1 阶段强引导造成的伪影。
ss_sampling_steps可选
类型integer
默认值12
1 – 50
用于创建初始结构的去噪步数。
ss_rescale_t可选
类型number
默认值5
1 – 6
生成结构时使用的噪声调度锐度。
slat_guidance_strength可选
类型number
默认值7.5
0 – 10
SLat 阶段引导强度;仅在 ss_source 设置为 'mesh' 时适用。
slat_guidance_rescale可选
类型number
默认值0.5
0 – 1
SLat 阶段引导重缩放;仅在 ss_source 设置为 'mesh' 时适用。
slat_sampling_steps可选
类型integer
默认值12
1 – 50
SLat 阶段采样步数;仅在 ss_source 设置为 'mesh' 时适用。
slat_rescale_t可选
类型number
默认值3
1 – 6
SLat 阶段 Rescale T 设置;仅在 ss_source 设置为 'mesh' 时适用。
shape_slat_guidance_strength可选
类型number
默认值7.5
0 – 10
生成详细几何结构时对输入的遵循程度。
shape_slat_guidance_rescale可选
类型number
默认值0.5
0 – 1
减少形状阶段强引导造成的伪影。
shape_slat_sampling_steps可选
类型integer
默认值12
1 – 50
用于细化形状的去噪步数。
shape_slat_rescale_t可选
类型number
默认值3
1 – 6
细化形状时使用的噪声调度锐度。
tex_slat_guidance_strength可选
类型number
默认值1
0 – 10
纹理对输入颜色的遵循程度。
tex_slat_guidance_rescale可选
类型number
默认值0
0 – 1
减少纹理阶段强引导造成的伪影。
tex_slat_sampling_steps可选
类型integer
默认值12
1 – 50
用于生成纹理的去噪步数。
tex_slat_rescale_t可选
类型number
默认值3
1 – 6
生成纹理时使用的噪声调度锐度。
decimation_target可选
类型integer
默认值500000
5,000 – 2,000,000
完成网格所需的顶点数量。
texture_size可选
类型enum
默认值2048
102420484096
用于网格烘焙纹理图像的分辨率。
image_urls必填
images
类型url[] · image
≤ 16 个项目
提供用于 3D 重建的视图:单个视图即可,但额外视图可提升质量,且所有视图必须展示同一对象。
multi_image_strategy可选
类型enum
默认值adaptive_guidance_weight
average_rightweighted_averagesequentialaverageadaptive_guidance_weightfixed_guidance_rescale
多视图条件策略,仅适用于多张输入图像;大多数情况下选择 'adaptive_guidance_weight' 可获得最佳结果。
enable_safety_checker可选
类型boolean
默认值true
调用方获得授权后,可将其设置为 false 以放宽审核。
必填
规范名称,也可接受
文件参数接受公开的 https:// URL、最大 10 MB 的 data: URI,或上传文件的 file_url。 文件
输出
GET /requests/req_8k2m4q7x1z9c3v5b6n0p2r4t
{
"id": "req_8k2m4q7x1z9c3v5b6n0p2r4t",
"object": "request",
"model": "reconviagen-0.5",
"status": "succeeded",
"created_at": "2026-10-20T12:00:00Z",
"started_at": "2026-10-20T12:00:02Z",
"completed_at": "2026-10-20T12:00:09Z",
"queue_position": null,
"cost": {
"credits": 300,
"usd": "0.30",
"charged": 300,
"refunded": 0
},
"input": {
"image_urls": [
"https://content.doitong.com/images/showcase/v3/gen_fox_cartoon.jpg"
]
},
"output": {
"model_glb": {
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_name": "0-output.jpeg",
"file_size": 1843200
},
"seed": 42
},
"outputs": [
{
"type": "file",
"url": "https://content.doitong.com/dev-api/2026/10/20/req_8k2m4q7x1z9c3v5b6n0p2r4t/0-output.jpeg",
"content_type": "image/jpeg",
"file_size": 1843200,
"stored": true
}
],
"error": null,
"metrics": {
"inference_seconds": 6.8
},
"urls": {
"self": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t",
"cancel": "https://api.doitong.com/v1/requests/req_8k2m4q7x1z9c3v5b6n0p2r4t/cancel"
}
}
你的文件,由你选择每个 outputs[].url 都是存储中的副本:可永久保存(按 GB-月付费),也可免费保存 7 天,由账户设置或 ?storage= 决定。
每个模型的结构相同图像、视频和音频均包含 type、url、content_type、width、height 和 duration。output 是模型自身的结果对象。
输出字段
- model_glb
- object {url, content_type, file_name, file_size}生成的 GLB,包含带 PBR 材质的 3D 模型。
- seed
- integer生成过程中应用的种子。
更多模型:图像转 3D
对于 AI 代理:此页面的 Markdown 版本位于 https://doitong.com/zh/developers/models/reconviagen-0.5.md. 所有模型和指南: https://doitong.com/developers/llms.txt