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Copy pathnodes_depth_anything_3.py
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681 lines (583 loc) · 30.7 KB
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"""ComfyUI nodes for Depth Anything 3.
Model capability matrix:
Variant head_type has_sky has_conf cam_dec
DA3-Small dualdpt False True yes
DA3-Base dualdpt False True yes
DA3-Mono-Large dpt True False no
DA3-Metric-Large dpt True False no (raw output is metres)
"""
from __future__ import annotations
import logging
from typing_extensions import override
import torch
import comfy.model_management as mm
import comfy.sd
import folder_paths
from comfy.ldm.colormap import turbo as _turbo
from comfy.ldm.depth_anything_3 import preprocess as da3_preprocess
from comfy_api.latest import ComfyExtension, Types, io
from comfy.ldm.moge.geometry import triangulate_grid_mesh
DA3ModelType = io.Custom("DA3_MODEL")
DA3Geometry = io.Custom("DA3_GEOMETRY")
DA3PointCloud = io.Custom("DA3_POINT_CLOUD")
# DA3_GEOMETRY is a dict with these optional keys (absent when the upstream model didn't produce them):
#
# Per-frame tensors - B = batch size in mono mode; B = S (number of views) in multi-view mode.
# "depth": torch.Tensor (B, H, W) -- raw model depth (always present; matches MoGe convention)
# "image": torch.Tensor (B, H, W, 3) -- source image in [0, 1], CPU (always present)
# "mode": str -- "mono" or "multiview" (always present)
# "sky": torch.Tensor (B, H, W) -- sky probability in [0, 1] (Mono/Metric variants only)
# "confidence": torch.Tensor (B, H, W) -- raw model confidence output (Small/Base variants only)
#
# Multi-view only - S = number of views; the leading 1 is the scene dimension from the model.
# "extrinsics": torch.Tensor (1, S, 3, 4) -- world-to-camera [R|t] matrices
# "intrinsics": torch.Tensor (1, S, 3, 3) -- pixel-space intrinsics
#
# DA3_POINT_CLOUD is a dict:
# "points": torch.Tensor (N, 3) -- 3-D coords in glTF convention (Y-up, Z-back)
# "colors": torch.Tensor (N, 3) -- RGB in [0, 1], or None
# "confidence": torch.Tensor (N,) -- raw confidence per point, or None
def _da3_unproject(depth: torch.Tensor, K: torch.Tensor) -> torch.Tensor:
"""Pixel-space K⁻¹ unprojection: (H,W) depth → (H,W,3) point map in OpenCV space."""
H, W = depth.shape
u = torch.arange(W, dtype=torch.float32, device=depth.device)
v = torch.arange(H, dtype=torch.float32, device=depth.device)
u, v = torch.meshgrid(u, v, indexing='xy') # both (H, W)
pix = torch.stack([u, v, torch.ones_like(u)], dim=-1) # (H, W, 3)
rays = torch.einsum('ij,hwj->hwi', torch.linalg.inv(K.to(depth.device)), pix)
return rays * depth.unsqueeze(-1) # (H, W, 3)
def _da3_default_K(H: int, W: int) -> torch.Tensor:
"""Fallback ~60° FOV pinhole K for mono-mode DA3 (no intrinsics in geometry)."""
fx = fy = float(W) * 0.7
return torch.tensor([[fx, 0.0, (W - 1) / 2.0],
[0.0, fy, (H - 1) / 2.0],
[0.0, 0.0, 1.0]], dtype=torch.float32)
def _da3_get_K(geometry: dict, b: int, H: int, W: int) -> torch.Tensor:
"""Return pixel-space K for batch element b, falling back to a default estimate."""
if "intrinsics" in geometry:
# shape (1, S, 3, 3) - leading scene dimension from the multiview head
return geometry["intrinsics"][0, b].float()
logging.getLogger("comfy").warning(
"DA3_GEOMETRY has no intrinsics (mono-mode model). "
"Using a ~60° FOV estimate; 3-D reconstruction may be inaccurate."
)
return _da3_default_K(H, W)
def _da3_get_extrinsic(geometry: dict, b: int) -> torch.Tensor | None:
"""Return the world-to-camera extrinsic for batch element b, or None in mono mode.
The model outputs (1, S, 3, 4) [R|t] matrices; the fallback identity is (4, 4).
_da3_apply_extrinsic handles both shapes via [:3, :3] / [:3, 3] slicing.
"""
if "extrinsics" not in geometry:
return None
return geometry["extrinsics"][0, b].float()
def _da3_apply_extrinsic(points_cam: torch.Tensor, E: torch.Tensor) -> torch.Tensor:
"""Transform (H,W,3) OpenCV camera-space points to world space."""
E = E.to(points_cam.device).float()
if not torch.isfinite(E).all():
logging.getLogger("comfy").warning(
"DA3 extrinsic matrix contains non-finite values (pose estimation may have failed). "
"Falling back to camera-space coordinates."
)
return points_cam
H, W, _ = points_cam.shape
R = E[:3, :3] # (3, 3) rotation
t = E[:3, 3] # (3,) translation
R_inv = R.T # rotation inverse = transpose for orthogonal R
t_inv = -(R_inv @ t) # (3,)
pts = points_cam.reshape(-1, 3) # (N, 3)
pts_world = pts @ R_inv.T + t_inv # (N, 3)
return pts_world.reshape(H, W, 3)
def _normalize_confidence(conf: torch.Tensor) -> torch.Tensor:
"""Map raw confidence to [0, 1] per image."""
B = conf.shape[0]
out = []
for i in range(B):
c = conf[i]
c_min, c_max = c.min(), c.max()
out.append((c - c_min) / (c_max - c_min) if c_max > c_min else torch.ones_like(c))
return torch.stack(out, dim=0)
def _da3_build_mask(geometry: dict, b: int, H: int, W: int, confidence_threshold: float, use_sky_mask: bool) -> torch.Tensor:
"""Build (H,W) bool keep-mask from sky probability and confidence."""
mask = torch.ones(H, W, dtype=torch.bool)
if use_sky_mask and "sky" in geometry:
mask = mask & (geometry["sky"][b] < 0.5)
if "confidence" in geometry and confidence_threshold > 0.0:
conf_norm = _normalize_confidence(geometry["confidence"][b:b + 1])[0]
mask = mask & (conf_norm >= confidence_threshold)
return mask
class LoadDA3Model(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="LoadDA3Model",
display_name="Load Depth Anything 3",
category="model/loaders",
inputs=[
io.Combo.Input(
"model_name",
options=folder_paths.get_filename_list("geometry_estimation"),
),
io.Combo.Input(
"weight_dtype",
options=["default", "fp16", "bf16", "fp32"],
default="default",
),
],
outputs=[DA3ModelType.Output()],
)
@classmethod
def execute(cls, model_name, weight_dtype) -> io.NodeOutput:
model_options = {}
if weight_dtype == "fp16":
model_options["dtype"] = torch.float16
elif weight_dtype == "bf16":
model_options["dtype"] = torch.bfloat16
elif weight_dtype == "fp32":
model_options["dtype"] = torch.float32
path = folder_paths.get_full_path_or_raise("geometry_estimation", model_name)
model = comfy.sd.load_diffusion_model(path, model_options=model_options)
return io.NodeOutput(model)
def _run_da3(model_patcher, image: torch.Tensor, process_res: int, method: str = "upper_bound_resize"):
"""Run DA3 on (B,H,W,3), returns depth/conf/sky at original resolution (or None)."""
assert image.ndim == 4 and image.shape[-1] == 3, f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
B, H, W, _ = image.shape
mm.load_model_gpu(model_patcher)
diffusion = model_patcher.model.diffusion_model
device = mm.get_torch_device()
dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
depths, confs, skies = [], [], []
for i in range(B):
single = image[i:i + 1].to(device)
x = da3_preprocess.preprocess_image(single, process_res=process_res, method=method)
x = x.to(dtype=dtype)
with torch.no_grad():
out = diffusion(x)
depth_lr = out["depth"]
depth_full = torch.nn.functional.interpolate(
depth_lr.unsqueeze(1).float(), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
depths.append(depth_full)
if "depth_conf" in out:
conf_full = torch.nn.functional.interpolate(
out["depth_conf"].unsqueeze(1).float(), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
confs.append(conf_full)
if "sky" in out:
sky_full = torch.nn.functional.interpolate(
out["sky"].unsqueeze(1).float(), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
skies.append(sky_full)
depth = torch.cat(depths, dim=0)
confidence = torch.cat(confs, dim=0) if confs else None
sky = torch.cat(skies, dim=0) if skies else None
return depth, confidence, sky
class DA3Inference(io.ComfyNode):
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DA3Inference",
search_aliases=["depth", "geometry", "da3", "depth anything", "monocular", "pointmap", "sky", "3d", "metric depth", "disparity"],
display_name="Run Depth Anything 3",
category="image/geometry estimation",
description="Run Depth Anything 3 on an image. In multi-view mode each image is treated as a separate view of the same scene.",
inputs=[
DA3ModelType.Input("da3_model"),
io.Image.Input("image"),
io.Int.Input("resolution", default=504, min=140, max=2520, step=14,
tooltip="Resolution the model runs at (longest side, multiple of 14).\n"
"Lower = faster / less VRAM.\n"
"Higher = more detail.\n"
"Output is upsampled back to the original size."),
io.Combo.Input("resize_method", options=["upper_bound_resize", "lower_bound_resize"], default="upper_bound_resize",
tooltip="upper_bound_resize: scale so the longest side = resolution (caps memory, default).\n"
"lower_bound_resize: scale so the shortest side = resolution (preserves more detail on tall/wide images, uses more memory)."),
io.DynamicCombo.Input("mode", tooltip="mono: single view image (works with any model variant).\n"
"multiview: all images processed together for geometric consistency + camera pose (for Small/Base models only).",
options=[
io.DynamicCombo.Option("mono", []),
io.DynamicCombo.Option("multiview", [
io.Combo.Input("ref_view_strategy", options=["saddle_balanced", "saddle_sim_range", "first", "middle"], default="saddle_balanced",
tooltip="Which view acts as the geometric anchor.\n"
"- saddle_balanced: the view most 'average' across all others (best general choice).\n"
"- saddle_sim_range: the view most visually distinct from the others.\n"
"- first / middle: fixed positional picks."),
io.Combo.Input("pose_method", options=["cam_dec", "ray_pose"], default="cam_dec",
tooltip="How the camera field-of-view is estimated (for Small/Base models only).\n"
"- cam_dec: learned from image features.\n"
"- ray_pose: derived geometrically from the model's 3D ray output.\n"
"Affects perspective correctness of the 3D output. Try both if results look distorted."),
]),
]),
],
outputs=[
DA3Geometry.Output("da3_geometry", tooltip="Dictionary of non-normalized tensors.\n"
"Always has the keys: depth, image, mode.\n"
"Optional keys: sky (for Mono/Metric), confidence (for Small/Base), extrinsics + intrinsics (for multi-view)."),
],
)
@classmethod
def execute(cls, da3_model, image, resolution, resize_method, mode) -> io.NodeOutput:
mode_val = mode["mode"] # "mono" or "multiview"
if mode_val == "mono":
return cls._execute_mono(da3_model, image, resolution, resize_method)
# Capability checks for multi-view mode.
diffusion = da3_model.model.diffusion_model
pose_method = mode["pose_method"]
ref_view_strategy = mode["ref_view_strategy"]
has_cam_dec = diffusion.cam_dec is not None
has_dualdpt = diffusion.head_type == "dualdpt"
if not has_cam_dec and not has_dualdpt:
raise ValueError(
"multi-view mode requires Small or Base model. The loaded model "
f"(head_type='{diffusion.head_type}') does not support cross-view "
"attention or camera pose estimation. Switch mode to 'mono', or "
"load Small or Base model for mult-view."
)
if pose_method == "cam_dec" and not has_cam_dec:
raise ValueError(
"pose_method='cam_dec' requires a camera decoder, but the loaded "
f"model (head_type='{diffusion.head_type}') does not have one. "
"Use pose_method='ray_pose' instead."
)
if pose_method == "ray_pose" and not has_dualdpt:
raise ValueError(
"pose_method='ray_pose' requires a DualDPT head, but the loaded "
f"model has a '{diffusion.head_type}' head. "
"Use pose_method='cam_dec' instead."
)
return cls._execute_multiview(
da3_model, image, resolution, resize_method,
ref_view_strategy, pose_method,
)
@classmethod
def _execute_mono(cls, model, image, resolution, resize_method) -> io.NodeOutput:
depth, confidence, sky = _run_da3(model, image, resolution, method=resize_method)
geometry: dict = {
"depth": depth.contiguous(),
"image": image[..., :3].cpu(),
"mode": "mono",
}
if sky is not None:
geometry["sky"] = sky.contiguous()
if confidence is not None:
geometry["confidence"] = confidence.contiguous()
return io.NodeOutput(geometry)
@classmethod
def _execute_multiview(cls, model, image, resolution, resize_method, ref_view_strategy, pose_method) -> io.NodeOutput:
assert image.ndim == 4 and image.shape[-1] == 3, \
f"expected (B,H,W,3) IMAGE; got {tuple(image.shape)}"
S, H, W, _ = image.shape
mm.load_model_gpu(model)
diffusion = model.model.diffusion_model
device = mm.get_torch_device()
dtype = diffusion.dtype if diffusion.dtype is not None else torch.float32
# All views in a single forward pass: (1, S, 3, H', W').
x = image.to(device)
x = da3_preprocess.preprocess_image(x, process_res=resolution, method=resize_method)
x = x.to(dtype=dtype).unsqueeze(0)
use_ray_pose = (pose_method == "ray_pose")
with torch.no_grad():
out = diffusion(x, use_ray_pose=use_ray_pose, ref_view_strategy=ref_view_strategy)
depth = torch.nn.functional.interpolate(
out["depth"].float().unsqueeze(1), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
sky = None
if "sky" in out:
sky = torch.nn.functional.interpolate(
out["sky"].unsqueeze(1).float(), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
if "extrinsics" in out and "intrinsics" in out:
extrinsics = out["extrinsics"].float().cpu()
intrinsics = out["intrinsics"].float().cpu()
else:
extrinsics = torch.eye(4)[None, None].expand(1, S, 4, 4).clone()
intrinsics = torch.eye(3)[None, None].expand(1, S, 3, 3).clone()
geometry: dict = {
"depth": depth.contiguous(),
"image": image[..., :3].cpu(),
"mode": "multiview",
"extrinsics": extrinsics.contiguous(),
"intrinsics": intrinsics.contiguous(),
}
if sky is not None:
geometry["sky"] = sky.contiguous()
if "depth_conf" in out:
conf = torch.nn.functional.interpolate(
out["depth_conf"].unsqueeze(1).float(), size=(H, W),
mode="bilinear", align_corners=False,
).squeeze(1).cpu()
geometry["confidence"] = conf.contiguous()
return io.NodeOutput(geometry)
class DA3Render(io.ComfyNode):
"""Render a visualization from a DA3_GEOMETRY packet."""
_DEPTH_RENDER_INPUTS = [
io.Combo.Input("normalization",
options=["v2_style", "min_max", "raw"],
default="v2_style",
tooltip="- v2_style: mean/std normalisation for perceptually balanced results (default).\n"
"- min_max: stretches the full depth range to [0, 1] for maximum contrast.\n"
"- raw: no scaling,preserves metric units for Metric model."),
io.Boolean.Input("apply_sky_clip", default=False,
tooltip="Clip sky-region depth to the 99th percentile of foreground depth before normalisation. "
"Requires a sky key in the da3_geometry input (for Mono/Metric models only)."),
]
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DA3Render",
display_name="Render Depth Anything 3",
category="image/geometry estimation",
description="Render a depth map, confidence map, or sky mask from Depth Anything 3 geometry data.",
inputs=[
DA3Geometry.Input("da3_geometry"),
io.DynamicCombo.Input("output",
tooltip="- depth: normalised greyscale depth image.\n"
"- depth_colored: depth mapped through the Turbo colormap.\n"
"- sky_mask: sky probability in [0, 1] (for Mono/Metric models only).\n"
"- confidence: normalised depth confidence (for Small/Base models only).",
options=[
io.DynamicCombo.Option("depth", cls._DEPTH_RENDER_INPUTS),
io.DynamicCombo.Option("depth_colored", cls._DEPTH_RENDER_INPUTS),
io.DynamicCombo.Option("sky_mask", [
io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the sky mask."),
]),
io.DynamicCombo.Option("confidence", [
io.Boolean.Input("colored", default=False, tooltip="Apply the Turbo colormap to the confidence map."),
]),
]),
],
outputs=[io.Image.Output()],
)
@classmethod
def execute(cls, da3_geometry, output) -> io.NodeOutput:
output_val = output["output"]
if output_val in ("depth", "depth_colored"):
normalization = output["normalization"]
apply_sky_clip = output["apply_sky_clip"]
if apply_sky_clip and "sky" not in da3_geometry:
raise ValueError(
"apply_sky_clip=True requires a sky tensor in the da3_geometry input, but none is present. "
"Run with Mono/Metric models or set apply_sky_clip=False."
)
depth = da3_geometry["depth"]
sky = da3_geometry.get("sky")
if apply_sky_clip and sky is not None:
depth = torch.stack([
da3_preprocess.apply_sky_aware_clip(depth[i], sky[i])
for i in range(depth.shape[0])
], dim=0)
grey = cls._depth_to_image(depth, sky, normalization) # (B,H,W,3) greyscale
result = _turbo(grey[..., 0]) if output_val == "depth_colored" else grey
elif output_val == "sky_mask":
if "sky" not in da3_geometry:
raise ValueError("geometry has no sky output; run with Mono/Metric models.")
sky = da3_geometry["sky"]
if output["colored"]:
result = _turbo(sky)
else:
result = sky.unsqueeze(-1).expand(*sky.shape, 3).contiguous()
elif output_val == "confidence":
if "confidence" not in da3_geometry:
raise ValueError("da3_geometry has no confidence output; run with Small/Base models.")
conf = _normalize_confidence(da3_geometry["confidence"])
if output["colored"]:
result = _turbo(conf)
else:
result = conf.unsqueeze(-1).expand(*conf.shape, 3).contiguous()
else:
raise ValueError(f"Unknown output mode: {output_val}")
return io.NodeOutput(result.float())
@staticmethod
def _depth_to_image(depth: torch.Tensor, sky_for_norm: torch.Tensor | None, normalization: str) -> torch.Tensor:
"""Normalise depth and pack as an (B,H,W,3) image tensor."""
N = depth.shape[0]
if normalization == "v2_style":
norm = torch.stack([
da3_preprocess.normalize_depth_v2_style(
depth[i], sky_for_norm[i] if sky_for_norm is not None else None)
for i in range(N)
], dim=0)
elif normalization == "min_max":
norm = da3_preprocess.normalize_depth_min_max(depth)
else:
norm = depth
out = norm.unsqueeze(-1).repeat(1, 1, 1, 3)
if normalization != "raw":
out = out.clamp(0.0, 1.0)
return out.contiguous()
class DA3GeometryToMesh(io.ComfyNode):
"""Convert a DA3_GEOMETRY packet into a Types.MESH by unprojecting depth and triangulating."""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DA3GeometryToMesh",
search_aliases=["da3", "depth anything", "mesh", "geometry", "3d", "triangulate"],
display_name="Convert DA3 Geometry to Mesh",
category="image/geometry estimation",
description="Convert a depth map into a triangulated 3D mesh.",
inputs=[
DA3Geometry.Input("da3_geometry"),
io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert. Per-image vertex counts differ so batches cannot be stacked."),
io.Int.Input("decimation", default=1, min=1, max=8, tooltip="Vertex stride. 1 = full resolution, 2 = half, etc."),
io.Float.Input("discontinuity_threshold", default=0.04, min=0.0, max=1.0, step=0.01, tooltip="Drop triangles whose 3x3 depth span exceeds this fraction. 0 = off."),
io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all, 1 = keep only the single most confident pixel). "
"Used when the geometry has a confidence map (Small/Base models)."),
io.Boolean.Input("use_sky_mask", default=True, tooltip="Exclude sky-probability pixels (sky >= 0.5) from the mesh. Used when the geometry has a sky map (Mono/Metric models)."),
io.Boolean.Input("texture", default=True, tooltip="Use the source image as a base color texture."),
],
outputs=[io.Mesh.Output()],
)
@classmethod
def execute(cls, da3_geometry, batch_index, decimation, discontinuity_threshold, confidence_threshold, use_sky_mask, texture) -> io.NodeOutput:
depth_all = da3_geometry["depth"] # (B, H, W)
B = depth_all.shape[0]
if batch_index >= B:
raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
depth = depth_all[batch_index] # (H, W)
H, W = depth.shape
# NaN/inf depth would propagate silently through unproject and produce an
# empty mesh; replace them with 0 here so those pixels are later excluded
# by the isfinite check inside triangulate_grid_mesh.
depth = depth.clone()
n_bad = (~torch.isfinite(depth)).sum().item()
if n_bad:
logging.getLogger("comfy").warning(
f"DA3GeometryToMesh: depth[{batch_index}] has {n_bad} non-finite pixels "
f"({100*n_bad/(H*W):.1f}%) - zeroed before unproject."
)
depth[~torch.isfinite(depth)] = 0.0
logging.getLogger("comfy").debug(
f"DA3GeometryToMesh: depth[{batch_index}] range "
f"[{depth.min():.4g}, {depth.max():.4g}], mean={depth.mean():.4g}"
)
K = _da3_get_K(da3_geometry, batch_index, H, W)
points = _da3_unproject(depth, K) # (H, W, 3) in OpenCV camera space
# Apply world-to-camera inverse so multi-view frames share a common world frame.
E = _da3_get_extrinsic(da3_geometry, batch_index)
if E is not None:
points = _da3_apply_extrinsic(points, E)
# Mask invalid pixels by setting them to inf so triangulate_grid_mesh skips them.
mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
# Also exclude pixels where depth was invalid.
mask = mask & (depth_all[batch_index] > 0) & torch.isfinite(depth_all[batch_index])
points = points.clone()
points[~mask] = float('inf')
verts, faces, uvs = triangulate_grid_mesh(
points,
decimation=decimation,
discontinuity_threshold=discontinuity_threshold,
depth=depth,
)
if verts.shape[0] == 0 or faces.shape[0] == 0:
raise ValueError(
"DA3GeometryToMesh produced an empty mesh. "
"Try raising discontinuity_threshold, lowering confidence_threshold, "
"or disabling use_sky_mask."
)
# OpenCV (X right, Y down, Z forward) → glTF (X right, Y up, Z back).
# Same transform as MoGePointMapToMesh perspective branch.
verts = verts * torch.tensor([1.0, -1.0, -1.0], dtype=verts.dtype)
faces = faces[:, [0, 2, 1]].contiguous()
tex = da3_geometry["image"][batch_index:batch_index + 1] if texture else None
mesh = Types.MESH(
vertices=verts.unsqueeze(0),
faces=faces.unsqueeze(0),
uvs=uvs.unsqueeze(0),
texture=tex,
)
return io.NodeOutput(mesh)
class DA3GeometryToPointCloud(io.ComfyNode):
"""Unproject a DA3_GEOMETRY depth map into a filtered DA3_POINT_CLOUD."""
@classmethod
def define_schema(cls):
return io.Schema(
node_id="DA3GeometryToPointCloud",
search_aliases=["da3", "depth anything", "point cloud", "pointcloud", "3d", "geometry"],
display_name="Convert DA3 Geometry to Point Cloud",
category="image/geometry estimation",
description="Convert a depth map into a 3D point cloud.",
inputs=[
DA3Geometry.Input("da3_geometry"),
io.Int.Input("batch_index", default=0, min=0, max=4096, tooltip="Which image of a batch to convert."),
io.Float.Input("confidence_threshold", default=0.1, min=0.0, max=1.0, step=0.01,
tooltip="Exclude pixels whose per-image normalised confidence is below this value (0 = keep all). Used when the geometry has a confidence map (Small/Base models)."),
io.Boolean.Input("use_sky_mask", default=True,
tooltip="Exclude sky-probability pixels (sky >= 0.5). Used when the geometry has a sky map (Mono/Metric models)."),
io.Int.Input("downsample", default=1, min=1, max=16,
tooltip="Take every Nth pixel (1 = full resolution). Higher values give fewer points and faster processing."),
],
# TODO: add a proper PointCloud output type
outputs=[DA3PointCloud.Output(display_name="point_cloud")],
)
@classmethod
def execute(cls, da3_geometry, batch_index, confidence_threshold, use_sky_mask, downsample) -> io.NodeOutput:
depth_all = da3_geometry["depth"] # (B, H, W)
B = depth_all.shape[0]
if batch_index >= B:
raise ValueError(f"batch_index {batch_index} is out of range; DA3_GEOMETRY has batch size {B}.")
depth = depth_all[batch_index].clone() # (H, W)
depth[~torch.isfinite(depth)] = 0.0
H, W = depth.shape
K = _da3_get_K(da3_geometry, batch_index, H, W)
if downsample > 1:
depth = depth[::downsample, ::downsample].contiguous()
# Scale intrinsics to the downsampled grid.
K = K.clone()
K[0, :] /= downsample
K[1, :] /= downsample
H_ds, W_ds = depth.shape
points = _da3_unproject(depth, K) # (H_ds, W_ds, 3) in OpenCV camera space
# Apply world-to-camera inverse so multi-view frames share a common world frame.
E = _da3_get_extrinsic(da3_geometry, batch_index)
if E is not None:
points = _da3_apply_extrinsic(points, E)
# Rebuild mask at downsampled resolution.
mask = _da3_build_mask(da3_geometry, batch_index, H, W, confidence_threshold, use_sky_mask)
if downsample > 1:
mask = mask[::downsample, ::downsample]
mask = mask & torch.isfinite(depth)
# OpenCV → glTF: flip Y and Z.
points_gltf = points.clone()
points_gltf[..., 1] *= -1.0
points_gltf[..., 2] *= -1.0
pts_flat = points_gltf.reshape(-1, 3)[mask.reshape(-1)]
colors_flat = None
if "image" in da3_geometry:
img = da3_geometry["image"][batch_index] # (H, W, 3)
if downsample > 1:
img = img[::downsample, ::downsample]
colors_flat = img.reshape(-1, 3)[mask.reshape(-1)]
conf_flat = None
if "confidence" in da3_geometry:
conf = da3_geometry["confidence"][batch_index] # (H, W)
if downsample > 1:
conf = conf[::downsample, ::downsample]
conf_flat = conf.reshape(-1)[mask.reshape(-1)]
if pts_flat.shape[0] == 0:
raise ValueError(
"DA3GeometryToPointCloud produced zero points after filtering. "
"Try lowering confidence_threshold or disabling use_sky_mask."
)
return io.NodeOutput({
"points": pts_flat,
"colors": colors_flat,
"confidence": conf_flat,
})
class DA3Extension(ComfyExtension):
@override
async def get_node_list(self) -> list[type[io.ComfyNode]]:
return [
LoadDA3Model,
DA3Inference,
DA3Render,
DA3GeometryToMesh,
# DA3GeometryToPointCloud, # Keep this commented out for now until we have a proper PointCloud output type
]
async def comfy_entrypoint() -> DA3Extension:
return DA3Extension()
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