Docs

README.md

@@ -4,58 +4,24 @@
 
 This is a **tiny** tootlbox  for **accelerating** NeRF training & rendering using PyTorch CUDA extensions. Plug-and-play for most of the NeRFs!
 
-## Examples: Instant-NGP NeRF
+## Examples: 
 
 ``` bash
+# Instant-NGP NeRF
 python examples/train_ngp_nerf.py --train_split trainval --scene lego
 ```
 
-Performance:
-
-| PSNR | Lego | Mic | Materials | Chair | Hotdog |
-| - | - | - | - | - | - |
-| Papers (5mins) | 36.39 | 36.22 | 29.78 | 35.00 | 37.40 |
-| Ours (~5mins)  | 36.61 | 37.62 | 30.11 | 36.09 | 38.09 |
-| Exact training time  | 300s  | 274s  | 266s  | 341s  | 277s  |
-
-
-## Examples: Vanilla MLP NeRF
-
 ``` bash
+# Vanilla MLP NeRF
 python examples/train_mlp_nerf.py --train_split train --scene lego
 ```
 
-Performance:
-
-| PNSR | Lego | Mic | Materials | Chair | Hotdog |
-| - | - | - | - | - | - |
-| Paper (~2days) | 32.54 | 32.91 | 29.62 | 33.00 | 36.18 |
-| Ours (~45mins) | 33.21 | 33.36 | 29.48 | 32.79 | 35.54 |
-
-## Examples: MLP NeRF on Dynamic objects (D-NeRF)
-
 ```bash
+# MLP NeRF on Dynamic objects (D-NeRF)
 python examples/train_mlp_dnerf.py --train_split train --scene lego
 ```
 
-Performance:
-
-|  | Lego | Stand Up |
-| - | - | - |
-| Paper (~2days) | 21.64 | 32.79 |
-| Ours (~45mins) | 24.66 | 33.98 |
-
-
-## Examples: NGP on unbounded scene
-
-On MipNeRF360 Garden scene
-
 ```bash
-python examples/train_ngp_nerf.py --train_split train --scene garden --aabb="-4,-4,-4,4,4,4" --unbounded --cone_angle=0.004
+# NGP on MipNeRF360 unbounded scene
+python examples/train_ngp_nerf.py --train_split train --scene garden --auto_aabb --unbounded --cone_angle=0.004
 ```
-
-Performance:
-
-|  | Garden |
-| - | - |
-| Ours | 25.13 |

docs/source/examples/dnerf.rst

@@ -1,2 +1,15 @@
 Dynamic Scene
-====================
+====================
+
+
+
++----------------------+----------+---------+-------+---------+-------+--------+---------+-------+
+|                      | bouncing | hell    | hook  | jumping | lego  | mutant | standup | trex  |
+|                      | balls    | warrior |       | jacks   |       |        |         |       |
++======================+==========+=========+=======+=========+=======+========+=========+=======+
+| Paper (PSNR: ~2day)  | 38.93    | 25.02   | 29.25 | 32.80   | 21.64 | 31.29  | 32.79   | 31.75 |
++----------------------+----------+---------+-------+---------+-------+--------+---------+-------+
+| Ours  (PSNR: ~50min) | 39.60    | 22.41   | 30.64 | 29.79   | 24.75 | 35.20  | 34.50   | 31.83 | 
++----------------------+----------+---------+-------+---------+-------+--------+---------+-------+
+| Ours  (Training time)| 45min    | 49min   | 51min | 46min   | 53min | 57min  | 49min   | 46min |
++----------------------+----------+---------+-------+---------+-------+--------+---------+-------+

docs/source/examples/unbounded.rst

@@ -1,2 +1,14 @@
 Unbounded Scene
-====================
+====================
+
+
++----------------------+-------+
+|                      | Garden| 
+|                      |       | 
++======================+=======+
+| Paper (PSNR: ~ days) | 26.98 |
++----------------------+-------+
+| Ours  (PSNR: ~ 1 hr) | 25.41 |
++----------------------+-------+
+| Ours  (Training time)| 58min |
++----------------------+-------+

docs/source/examples/vanilla.rst

@@ -11,11 +11,11 @@ Benchmarks
 |                      | Lego  | Mic   | Materials  |Chair  |Hotdog  | Ficus  | Drums  | Ship   |
 |                      |       |       |            |       |        |        |        |        |
 +======================+=======+=======+============+=======+========+========+========+========+
-| Paper (PSNR: 5min)   | 32.54 | 32.91 | 29.62      | 33.00 | 36.18  | 30.13  | 25.01  | 28.65  |
+| Paper (PSNR: 1~2days)| 32.54 | 32.91 | 29.62      | 33.00 | 36.18  | 30.13  | 25.01  | 28.65  |
 +----------------------+-------+-------+------------+-------+--------+--------+--------+--------+
-| Ours  (PSNR)         | XX.XX | XX.XX | XX.XX      | XX.XX | XX.XX  | XX.XX  | XX.XX  | XX.XX  |
+| Ours  (PSNR: ~50min) | 33.69 | 33.76 | 29.73      | 33.32 | 35.80  | 32.52  | 25.39  | 28.18  |
 +----------------------+-------+-------+------------+-------+--------+--------+--------+--------+
-| Ours  (Training time)| XXmin | XXmin | XXmin      | 45min | XXmin  | XXmin  | 41min  | XXmin  |
+| Ours  (Training time)| 58min | 53min | 46min      | 62min | 56min  | 42min  | 52min  | 49min  |
 +----------------------+-------+-------+------------+-------+--------+--------+--------+--------+
 
 .. _`github repository`: : https://github.com/KAIR-BAIR/nerfacc/

examples/datasets/nerf_360_v2.py

@@ -144,6 +144,12 @@ class SubjectLoader(torch.utils.data.Dataset):
     SPLITS = ["train", "test"]
     SUBJECT_IDS = [
         "garden",
+        "bicycle",
+        "bonsai",
+        "counter",
+        "kitchen",
+        "room",
+        "stump",
     ]
 
     OPENGL_CAMERA = False

examples/radiance_fields/ngp.py

@@ -34,6 +34,31 @@ def backward(ctx, g):  # pylint: disable=arguments-differ
 trunc_exp = _TruncExp.apply
 
 
+def contract_to_unisphere(
+    x: torch.Tensor,
+    aabb: torch.Tensor,
+    eps: float = 1e-6,
+    derivative: bool = False,
+):
+    aabb_min, aabb_max = torch.split(aabb, 3, dim=-1)
+    x = (x - aabb_min) / (aabb_max - aabb_min)
+    x = x * 2 - 1  # aabb is at [-1, 1]
+    mag = x.norm(dim=-1, keepdim=True)
+    mask = mag.squeeze(-1) > 1
+
+    if derivative:
+        dev = (2 * mag - 1) / mag**2 + 2 * x**2 * (
+            1 / mag**3 - (2 * mag - 1) / mag**4
+        )
+        dev[~mask] = 1.0
+        dev = torch.clamp(dev, min=eps)
+        return dev
+    else:
+        x[mask] = (2 - 1 / mag[mask]) * (x[mask] / mag[mask])
+        x = x / 4 + 0.5  # [-inf, inf] is at [0, 1]
+        return x
+
+
 class NGPradianceField(torch.nn.Module):
     """Instance-NGP radiance Field"""
 
@@ -114,31 +139,18 @@ def __init__(
 
     def query_opacity(self, x, step_size):
         density = self.query_density(x)
-        aabb_min, aabb_max = torch.split(self.aabb, self.num_dim, dim=-1)
         if self.unbounded:
-            # TODO: [revisit] is this necessary?
-            # 1.0 / derivative of tanh contraction
-            x = (x - aabb_min) / (aabb_max - aabb_min)
-            x = x - 0.5
-            scaling = 1.0 / (
-                torch.clamp(1.0 - torch.tanh(x) ** 2, min=1e6) * 0.5
-            )
-            scaling = scaling * (aabb_max - aabb_min)
-        else:
-            scaling = aabb_max - aabb_min
-        step_size = step_size * scaling.norm(dim=-1, keepdim=True)
-        # if the density is small enough those two are the same.
-        # opacity = 1.0 - torch.exp(-density * step_size)
+            # NOTE: In principle, we should use the following formula to scale
+            # up the step size, but in practice, it is somehow not helpful.
+            # derivitive = contract_to_unisphere(x, self.aabb, derivative=True)
+            # step_size = step_size / derivitive.norm(dim=-1, keepdim=True)
+            pass
         opacity = density * step_size
         return opacity
 
     def query_density(self, x, return_feat: bool = False):
         if self.unbounded:
-            # tanh contraction
-            aabb_min, aabb_max = torch.split(self.aabb, self.num_dim, dim=-1)
-            x = (x - aabb_min) / (aabb_max - aabb_min)
-            x = x - 0.5
-            x = (torch.tanh(x) + 1) * 0.5
+            x = contract_to_unisphere(x, self.aabb)
         else:
             aabb_min, aabb_max = torch.split(self.aabb, self.num_dim, dim=-1)
             x = (x - aabb_min) / (aabb_max - aabb_min)

examples/train_ngp_nerf.py

@@ -42,6 +42,12 @@
             "ship",
             # mipnerf360 unbounded
             "garden",
+            "bicycle",
+            "bonsai",
+            "counter",
+            "kitchen",
+            "room",
+            "stump",
         ],
         help="which scene to use",
     )
@@ -61,58 +67,27 @@
         action="store_true",
         help="whether to use unbounded rendering",
     )
+    parser.add_argument(
+        "--auto_aabb",
+        action="store_true",
+        help="whether to automatically compute the aabb",
+    )
     parser.add_argument("--cone_angle", type=float, default=0.0)
     args = parser.parse_args()
 
     render_n_samples = 1024
 
-    # setup the scene bounding box.
-    if args.unbounded:
-        print("Using unbounded rendering")
-        contraction_type = ContractionType.UN_BOUNDED_SPHERE
-        # contraction_type = ContractionType.UN_BOUNDED_TANH
-        scene_aabb = None
-        near_plane = 0.2
-        far_plane = 1e4
-        render_step_size = 1e-2
-    else:
-        contraction_type = ContractionType.AABB
-        scene_aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device)
-        near_plane = None
-        far_plane = None
-        render_step_size = (
-            (scene_aabb[3:] - scene_aabb[:3]).max()
-            * math.sqrt(3)
-            / render_n_samples
-        ).item()
-
-    # setup the radiance field we want to train.
-    max_steps = 20000
-    grad_scaler = torch.cuda.amp.GradScaler(2**10)
-    radiance_field = NGPradianceField(
-        aabb=args.aabb,
-        unbounded=args.unbounded,
-    ).to(device)
-    optimizer = torch.optim.Adam(
-        radiance_field.parameters(), lr=1e-2, eps=1e-15
-    )
-    scheduler = torch.optim.lr_scheduler.MultiStepLR(
-        optimizer,
-        milestones=[max_steps // 2, max_steps * 3 // 4, max_steps * 9 // 10],
-        gamma=0.33,
-    )
-
     # setup the dataset
     train_dataset_kwargs = {}
     test_dataset_kwargs = {}
-    if args.scene == "garden":
+    if args.unbounded:
         from datasets.nerf_360_v2 import SubjectLoader
 
         data_root_fp = "/home/ruilongli/data/360_v2/"
         target_sample_batch_size = 1 << 20
         train_dataset_kwargs = {"color_bkgd_aug": "random", "factor": 4}
         test_dataset_kwargs = {"factor": 4}
-        grid_resolution = 128
+        grid_resolution = 256
     else:
         from datasets.nerf_synthetic import SubjectLoader
 
@@ -143,6 +118,51 @@
     test_dataset.camtoworlds = test_dataset.camtoworlds.to(device)
     test_dataset.K = test_dataset.K.to(device)
 
+    if args.auto_aabb:
+        camera_locs = torch.cat(
+            [train_dataset.camtoworlds, test_dataset.camtoworlds]
+        )[:, :3, -1]
+        args.aabb = torch.cat(
+            [camera_locs.min(dim=0).values, camera_locs.max(dim=0).values]
+        ).tolist()
+        print("Using auto aabb", args.aabb)
+
+    # setup the scene bounding box.
+    if args.unbounded:
+        print("Using unbounded rendering")
+        contraction_type = ContractionType.UN_BOUNDED_SPHERE
+        # contraction_type = ContractionType.UN_BOUNDED_TANH
+        scene_aabb = None
+        near_plane = 0.2
+        far_plane = 1e4
+        render_step_size = 1e-2
+    else:
+        contraction_type = ContractionType.AABB
+        scene_aabb = torch.tensor(args.aabb, dtype=torch.float32, device=device)
+        near_plane = None
+        far_plane = None
+        render_step_size = (
+            (scene_aabb[3:] - scene_aabb[:3]).max()
+            * math.sqrt(3)
+            / render_n_samples
+        ).item()
+
+    # setup the radiance field we want to train.
+    max_steps = 40000 if args.unbounded else 20000
+    grad_scaler = torch.cuda.amp.GradScaler(2**10)
+    radiance_field = NGPradianceField(
+        aabb=args.aabb,
+        unbounded=args.unbounded,
+    ).to(device)
+    optimizer = torch.optim.Adam(
+        radiance_field.parameters(), lr=1e-2, eps=1e-15
+    )
+    scheduler = torch.optim.lr_scheduler.MultiStepLR(
+        optimizer,
+        milestones=[max_steps // 2, max_steps * 3 // 4, max_steps * 9 // 10],
+        gamma=0.33,
+    )
+
     occupancy_grid = OccupancyGrid(
         roi_aabb=args.aabb,
         resolution=grid_resolution,
@@ -201,7 +221,7 @@
             optimizer.step()
             scheduler.step()
 
-            if step % 5000 == 0:
+            if step % 10000 == 0:
                 elapsed_time = time.time() - tic
                 loss = F.mse_loss(rgb[alive_ray_mask], pixels[alive_ray_mask])
                 print(

nerfacc/grid.py

@@ -177,6 +177,11 @@ def _update(
         x = (
             grid_coords + torch.rand_like(grid_coords, dtype=torch.float32)
         ) / self.resolution
+        if self._contraction_type == ContractionType.UN_BOUNDED_SPHERE:
+            # only the points inside the sphere are valid
+            mask = (x - 0.5).norm(dim=1) < 0.5
+            x = x[mask]
+            indices = indices[mask]
         # voxel coordinates [0, 1]^3 -> world
         x = contract_inv(
             x,