From 9ace02b3ae19f03edbe3458936a57c8e57152128 Mon Sep 17 00:00:00 2001
From: Matthew Tancik <tancik@berkeley.edu>
Date: Thu, 4 May 2023 10:46:51 -0700
Subject: [PATCH] Remove nerfacc aabb (#1874)

* Remove nerfacc aabb

* remove custom nerfacc logic

* lint
---
 nerfstudio/cameras/cameras.py |  4 ----
 nerfstudio/utils/math.py      | 41 +----------------------------------
 2 files changed, 1 insertion(+), 44 deletions(-)

diff --git a/nerfstudio/cameras/cameras.py b/nerfstudio/cameras/cameras.py
index 0dec377c9..9d0fb26d8 100644
--- a/nerfstudio/cameras/cameras.py
+++ b/nerfstudio/cameras/cameras.py
@@ -17,7 +17,6 @@ Camera Models
 """
 import base64
 import math
-import os
 from dataclasses import dataclass
 from enum import Enum, auto
 from typing import Dict, List, Optional, Tuple, Union
@@ -33,7 +32,6 @@ import nerfstudio.utils.poses as pose_utils
 from nerfstudio.cameras import camera_utils
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.data.scene_box import SceneBox
-from nerfstudio.utils.misc import strtobool
 from nerfstudio.utils.tensor_dataclass import TensorDataclass
 
 TORCH_DEVICE = Union[torch.device, str]  # pylint: disable=invalid-name
@@ -150,8 +148,6 @@ class Cameras(TensorDataclass):
 
         self.__post_init__()  # This will do the dataclass post_init and broadcast all the tensors
 
-        self._use_nerfacc = strtobool(os.environ.get("INTERSECT_WITH_NERFACC", "TRUE"))
-
     def _init_get_fc_xy(self, fc_xy: Union[float, torch.Tensor], name: str) -> torch.Tensor:
         """
         Parses the input focal length / principle point x or y and returns a tensor of the correct shape
diff --git a/nerfstudio/utils/math.py b/nerfstudio/utils/math.py
index 89a4d3250..74d4d4c00 100644
--- a/nerfstudio/utils/math.py
+++ b/nerfstudio/utils/math.py
@@ -21,8 +21,6 @@ import torch
 from torchtyping import TensorType
 from typing_extensions import Literal
 
-_USE_NERFACC = True
-
 
 def components_from_spherical_harmonics(levels: int, directions: TensorType[..., 3]) -> TensorType[..., "components"]:
     """
@@ -198,7 +196,7 @@ def expected_sin(x_means: torch.Tensor, x_vars: torch.Tensor) -> torch.Tensor:
 
 
 @torch.jit.script
-def _intersect_aabb(
+def intersect_aabb(
     origins: torch.Tensor,
     directions: torch.Tensor,
     aabb: torch.Tensor,
@@ -238,43 +236,6 @@ def _intersect_aabb(
     return t_min, t_max
 
 
-def intersect_aabb(
-    origins: TensorType["N", 3],
-    directions: TensorType["N", 3],
-    aabb: TensorType[6],
-) -> Tuple[TensorType["N"], TensorType["N"]]:
-    """
-    Implementation of ray intersection with AABB box
-
-    Args:
-        origins: 3d positions
-        directions: Normalized directions
-        aabb: array of aabb box in the form of [x_min, y_min, z_min, x_max, y_max, z_max]
-        max_bound: Maximum value of t_max
-        invalid_value: Value to return in case of no intersection
-
-    Returns:
-        t_min, t_max - two tensors of shapes N representing distance of intersection from the origin.
-    """
-
-    global _USE_NERFACC  # pylint: disable=global-statement
-    if _USE_NERFACC:
-        try:
-            import nerfacc  # pylint: disable=import-outside-toplevel
-
-            t_min, t_max, _ = nerfacc.ray_aabb_intersect(
-                origins, directions, aabb[None, :], near_plane=0, far_plane=1e10, miss_value=1e10
-            )
-            t_min, t_max = t_min.squeeze(-1), t_max.squeeze(-1)
-        except:  # pylint: disable=bare-except
-            t_min, t_max = _intersect_aabb(origins, directions, aabb, max_bound=1e10, invalid_value=1e10)
-            _USE_NERFACC = False
-    else:
-        t_min, t_max = _intersect_aabb(origins, directions, aabb, max_bound=1e10, invalid_value=1e10)
-
-    return t_min, t_max
-
-
 def safe_normalize(
     vectors: TensorType["batch_dim":..., "N"],
     eps: float = 1e-10,