diff --git a/pytorch3d/renderer/mesh/shader.py b/pytorch3d/renderer/mesh/shader.py
index 677812a7..72de786d 100644
--- a/pytorch3d/renderer/mesh/shader.py
+++ b/pytorch3d/renderer/mesh/shader.py
@@ -353,3 +353,71 @@ class SplatterPhongShader(ShaderBase):
         )
 
         return images
+
+
+class HardDepthShader(ShaderBase):
+    """
+    Renders the Z distances of the closest face for each pixel. If no face is
+    found it returns the zfar value of the camera.
+
+    Output from this shader is [N, H, W, 1] since it's only depth.
+
+    To use the default values, simply initialize the shader with the desired
+    device e.g.
+
+    .. code-block::
+
+        shader = HardDepthShader(device=torch.device("cuda:0"))
+    """
+
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
+        cameras = super()._get_cameras(**kwargs)
+
+        zfar = kwargs.get("zfar", getattr(cameras, "zfar", 100.0))
+        mask = fragments.pix_to_face < 0
+
+        zbuf = fragments.zbuf[..., 0].clone()
+        zbuf[mask] = zfar
+        return zbuf.unsqueeze(3)
+
+
+class SoftDepthShader(ShaderBase):
+    """
+    Renders the Z distances using an aggregate of the distances of each face
+    based off of the point distance.  If no face is found it returns the zfar
+    value of the camera.
+
+    Output from this shader is [N, H, W, 1] since it's only depth.
+
+    To use the default values, simply initialize the shader with the desired
+    device e.g.
+
+    .. code-block::
+
+        shader = SoftDepthShader(device=torch.device("cuda:0"))
+    """
+
+    def forward(self, fragments: Fragments, meshes: Meshes, **kwargs) -> torch.Tensor:
+        cameras = super()._get_cameras(**kwargs)
+
+        N, H, W, K = fragments.pix_to_face.shape
+        device = fragments.zbuf.device
+        mask = fragments.pix_to_face >= 0
+
+        zfar = kwargs.get("zfar", getattr(cameras, "zfar", 100.0))
+
+        # Sigmoid probability map based on the distance of the pixel to the face.
+        prob_map = torch.sigmoid(-fragments.dists / self.blend_params.sigma) * mask
+
+        # append extra face for zfar
+        dists = torch.cat(
+            (fragments.zbuf, torch.ones((N, H, W, 1), device=device) * zfar), dim=3
+        )
+        probs = torch.cat((prob_map, torch.ones((N, H, W, 1), device=device)), dim=3)
+
+        # compute weighting based off of probabilities using cumsum
+        probs = probs.cumsum(dim=3)
+        probs = probs.clamp(max=1)
+        probs = probs.diff(dim=3, prepend=torch.zeros((N, H, W, 1), device=device))
+
+        return (probs * dists).sum(dim=3).unsqueeze(3)
diff --git a/tests/test_shader.py b/tests/test_shader.py
index 3b751e8b..93ef71c7 100644
--- a/tests/test_shader.py
+++ b/tests/test_shader.py
@@ -10,9 +10,11 @@ import torch
 from pytorch3d.renderer.cameras import look_at_view_transform, PerspectiveCameras
 from pytorch3d.renderer.mesh.rasterizer import Fragments
 from pytorch3d.renderer.mesh.shader import (
+    HardDepthShader,
     HardFlatShader,
     HardGouraudShader,
     HardPhongShader,
+    SoftDepthShader,
     SoftPhongShader,
     SplatterPhongShader,
 )
@@ -24,9 +26,11 @@ from .common_testing import TestCaseMixin
 class TestShader(TestCaseMixin, unittest.TestCase):
     def setUp(self):
         self.shader_classes = [
+            HardDepthShader,
             HardFlatShader,
             HardGouraudShader,
             HardPhongShader,
+            SoftDepthShader,
             SoftPhongShader,
             SplatterPhongShader,
         ]