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Pulsar unified interface update #1369

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Pulsar unified interface update #1369

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64 changes: 37 additions & 27 deletions pytorch3d/renderer/points/pulsar/unified.py
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Original file line number Diff line number Diff line change
Expand Up @@ -206,7 +206,7 @@ def _extract_intrinsics( # noqa: C901
"The orthographic camera scale must be ((1.0, 1.0, 1.0),). "
f"{kwargs.get('scale_xyz', cameras.scale_xyz)[cloud_idx]}."
)
sensor_width = max_x - min_x
sensor_width = (max_x - min_x) * (self.renderer._renderer.width / self.renderer._renderer.height)
if not sensor_width > 0.0:
raise ValueError(
f"The orthographic camera must have positive size! Is: {sensor_width}." # noqa: B950
Expand All @@ -220,24 +220,25 @@ def _extract_intrinsics( # noqa: C901
focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[
cloud_idx
]
if (
focal_length_conf.numel() == 2
and focal_length_conf[0] * self.renderer._renderer.width
- focal_length_conf[1] * self.renderer._renderer.height
> 1e-5
):
if torch.any(focal_length_conf <= 0.0):
raise ValueError(f"Pulsar requires focal lengths > 0.0. Provided: {focal_length_conf}.")
if cameras.in_ndc():
focal_length_conf *= self.renderer._renderer.height / 2.0
if (focal_length_conf.numel() == 2 and abs(focal_length_conf[0] - focal_length_conf[1]) > 1e-5):
raise ValueError(
"Pulsar only supports a single focal length! "
"Provided: %s." % (str(focal_length_conf))
)
if focal_length_conf.numel() == 2:
sensor_width = 2.0 / focal_length_conf[0]
focal_length_px = focal_length_conf[0]
else:
if focal_length_conf.numel() != 1:
raise ValueError(
"Focal length not parsable: %s." % (str(focal_length_conf))
)
sensor_width = 2.0 / focal_length_conf
focal_length_px = focal_length_conf
focal_length_px /= self.renderer._renderer.width / 2.0
sensor_width = 2.0 / focal_length_px
if "znear" not in kwargs.keys() or "zfar" not in kwargs.keys():
raise ValueError(
"pulsar needs znear and zfar values for "
Expand All @@ -248,16 +249,19 @@ def _extract_intrinsics( # noqa: C901
zfar = kwargs["zfar"][cloud_idx]
principal_point_x = (
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0]
* 0.5
* self.renderer._renderer.width
)
principal_point_y = (
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][1]
* 0.5
* self.renderer._renderer.height
)
if cameras.in_ndc():
principal_point_x *= 0.5 * self.renderer._renderer.width * (self.renderer._renderer.height / self.renderer._renderer.width)
principal_point_y *= -0.5 * self.renderer._renderer.height
else:
principal_point_x = self.renderer._renderer.width / 2.0 - principal_point_x
principal_point_y -= self.renderer._renderer.height / 2.0
else:
if not isinstance(cameras, PerspectiveCameras):
# This currently means FoVPerspectiveCameras.
# Create a virtual focal length that is closer than znear.
znear = kwargs.get("znear", cameras.znear)[cloud_idx]
zfar = kwargs.get("zfar", cameras.zfar)[cloud_idx]
Expand All @@ -266,7 +270,10 @@ def _extract_intrinsics( # noqa: C901
afov = kwargs.get("fov", cameras.fov)[cloud_idx]
if kwargs.get("degrees", cameras.degrees):
afov *= math.pi / 180.0
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@classner This operation shouldn't be in-place I think!
Replacing afov *= math.pi / 180.0 with afov = afov * math.pi / 180.0 fixed issues with my code.
Thanks for this PR by the way!

sensor_width = math.tan(afov / 2.0) * 2.0 * focal_length
aspect_ratio = kwargs.get("aspect_ratio", cameras.aspect_ratio)[cloud_idx]
if aspect_ratio != 1.0:
raise ValueError(f"Pulsar only supports aspect ration 1.0! Provided: {aspect_ratio}.")
sensor_width = math.tan(afov / 2.0) * 2.0 * focal_length * (self.renderer._renderer.width / self.renderer._renderer.height)
if not (
kwargs.get("aspect_ratio", cameras.aspect_ratio)[cloud_idx]
- self.renderer._renderer.width / self.renderer._renderer.height
Expand All @@ -286,10 +293,13 @@ def _extract_intrinsics( # noqa: C901
focal_length_conf = kwargs.get("focal_length", cameras.focal_length)[
cloud_idx
]
if torch.any(focal_length_conf <= 0.0):
raise ValueError(f"Pulsar requires focal lengths > 0.0. Provided: {focal_length_conf}.")
if cameras.in_ndc():
focal_length_conf *= self.renderer._renderer.height / 2.0
if (
focal_length_conf.numel() == 2
and focal_length_conf[0] * self.renderer._renderer.width
- focal_length_conf[1] * self.renderer._renderer.height
and abs(focal_length_conf[0] - focal_length_conf[1])
> 1e-5
):
raise ValueError(
Expand All @@ -312,6 +322,7 @@ def _extract_intrinsics( # noqa: C901
"Focal length not parsable: %s." % (str(focal_length_conf))
)
focal_length_px = focal_length_conf
focal_length_px /= self.renderer._renderer.width / 2.0
focal_length = torch.tensor(
[
znear - 1e-6,
Expand All @@ -322,14 +333,16 @@ def _extract_intrinsics( # noqa: C901
sensor_width = focal_length / focal_length_px * 2.0
principal_point_x = (
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][0]
* 0.5
* self.renderer._renderer.width
)
principal_point_y = (
kwargs.get("principal_point", cameras.principal_point)[cloud_idx][1]
* 0.5
* self.renderer._renderer.height
)
if cameras.in_ndc():
principal_point_x *= 0.5 * self.renderer._renderer.width * (self.renderer._renderer.height / self.renderer._renderer.width)
principal_point_y *= -0.5 * self.renderer._renderer.height
else:
principal_point_x = self.renderer._renderer.width / 2.0 - principal_point_x
principal_point_y -= self.renderer._renderer.height / 2.0
focal_length = _ensure_float_tensor(focal_length, device)
sensor_width = _ensure_float_tensor(sensor_width, device)
principal_point_x = _ensure_float_tensor(principal_point_x, device)
Expand Down Expand Up @@ -373,7 +386,7 @@ def _extract_extrinsics(
return cam_pos, cam_rot

def _get_vert_rad(
self, vert_pos, cam_pos, orthogonal_projection, focal_length, kwargs, cloud_idx
self, vert_pos, cam_pos, orthogonal_projection, focal_length, sensor_width, kwargs, cloud_idx
) -> torch.Tensor:
"""
Get point radiuses.
Expand Down Expand Up @@ -403,12 +416,8 @@ def _get_vert_rad(
)
else:
point_dists = torch.norm((vert_pos - cam_pos), p=2, dim=1, keepdim=False)
vert_rad = raster_rad / focal_length.to(vert_pos.device) * point_dists
if isinstance(self.rasterizer.cameras, PerspectiveCameras):
# NDC normalization happens through adjusted focal length.
pass
else:
vert_rad = vert_rad / 2.0 # NDC normalization.
vert_rad = raster_rad / focal_length.to(vert_pos.device) * point_dists * sensor_width
vert_rad = vert_rad / 2.0 # NDC normalization.
return vert_rad

# point_clouds is not typed to avoid a cyclic dependency.
Expand Down Expand Up @@ -503,6 +512,7 @@ def forward(self, point_clouds, **kwargs) -> torch.Tensor:
cam_pos,
orthogonal_projection,
focal_length,
sensor_width,
kwargs,
cloud_idx,
)
Expand Down