adding near plane

nerfacc/utils.py

@@ -45,7 +45,8 @@ def volumetric_marching(
     t_min: Tensor = None,
     t_max: Tensor = None,
     render_step_size: float = 1e-3,
-    stratified: bool = False,
+    near_plane: float = 0.0,
+    stratified: bool = False
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
     """Volumetric marching with occupancy test.
 
@@ -64,6 +65,7 @@ def volumetric_marching(
         t_max: Optional. Ray far planes. Tensor with shape (n_ray,). \
             If not given it will be calculated using aabb test. Default is None.
         render_step_size: Marching step size. Default is 1e-3.
+        near_plane: Near plane of the camera. Default is 0.0.
         stratified: Whether to use stratified sampling. Default is False.
 
     Returns:
@@ -83,6 +85,8 @@ def volumetric_marching(
         raise NotImplementedError("Only support cuda inputs.")
     if t_min is None or t_max is None:
         t_min, t_max = ray_aabb_intersect(rays_o, rays_d, aabb)
+        if near_plane > 0.0:
+            t_min = torch.clamp(t_min, min=near_plane)
     assert (
         scene_occ_binary.numel()
         == scene_resolution[0] * scene_resolution[1] * scene_resolution[2]

nerfacc/volumetric_rendering.py

@@ -19,6 +19,7 @@ def volumetric_rendering(
     scene_resolution: Tuple[int, int, int],
     render_bkgd: torch.Tensor,
     render_step_size: int,
+    near_plane: float = 0.0,
     stratified: bool = False,
 ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
     """A *fast* version of differentiable volumetric rendering."""
@@ -48,6 +49,8 @@ def volumetric_rendering(
             scene_occ_binary=scene_occ_binary,
             # sampling
             render_step_size=render_step_size,
+            # optional settings
+            near_plane=near_plane,
             stratified=stratified,
         )
         frustum_positions = (

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "nerfacc"
-version = "0.0.4"
+version = "0.0.5"
 authors = [{name = "Ruilong", email = "[email protected]"}]
 license = { text="MIT" }
 requires-python = ">=3.8"