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41ff959 a6825eb 41ff959 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 | from dataclasses import dataclass
from pathlib import Path
from typing import Literal
import torch
import torch.nn.functional as F
from einops import rearrange
from torch import nn
from src.model.types import BatchedViews
from src.model.encoder.gaussian.gaussian_decoder import (
GaussianDecoder,
GaussianDecoderCfg,
)
from src.model.encoder.depth.depthpro.depthpro_wrapper import DepthPro
from src.model.encoder.depth.infinidepth.sampling_utils import (
SparseSamplingOutput,
make_sparse_surface_samples,
)
from src.model.encoder.gaussian.basic_encoder import BasicEncoder
from src.model.encoder.gaussian.implicit_gs_head import ImplicitGSHead
from src.utils.gaussians import Gaussians3D, unproject_gaussians
from src.model.encoder.encoder import Encoder
class DinoBasicImageFeatureBranch(nn.Module):
"""DINOv3 image branch with BasicEncoder low-level features.
Args:
backbone_type: DINOv3 backbone size identifier.
basic_dim: Output channels for the BasicEncoder branch.
"""
def __init__(
self,
backbone_type: str,
basic_dim: int,
) -> None:
super().__init__()
dinov3_layer_indices = {
"vitl16": [4, 11, 17, 23],
"vith16plus": [7, 15, 23, 31],
}
dinov3_repo_dir = (Path(__file__).resolve().parent / "blocks" / "torchhub" / "dinov3")
if backbone_type not in dinov3_layer_indices:
raise ValueError(f"Unsupported DINOv3 encoder: {backbone_type}")
self.backbone = torch.hub.load(
str(dinov3_repo_dir),
f"dinov3_{backbone_type}",
source="local",
pretrained=False,
)
self.layer_indices = dinov3_layer_indices[backbone_type]
self.patch_size = 16
self.hidden_dim = self.backbone.blocks[0].attn.qkv.in_features
self.basic_encoder = BasicEncoder(input_dim=3, output_dim=basic_dim, stride=4)
self.register_buffer(
"_mean",
torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1),
)
self.register_buffer(
"_std",
torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1),
)
def forward(self, image: torch.Tensor):
"""Extract DINOv3 tokens and BasicEncoder features.
Args:
image: RGB tensor with shape `[B, 3, H, W]` in `[0, 1]`.
Returns:
A tuple `(features, basic_feat, patch_h, patch_w)`, where `features`
are DINOv3 intermediate outputs, `basic_feat` has shape
`[B, C_basic, H / 4, W / 4]`, and `patch_h`, `patch_w` describe the
DINO patch grid.
"""
h, w = image.shape[-2:]
patch_h, patch_w = h // self.patch_size, w // self.patch_size
# DINO ViT-L is the heaviest forward pass; run in bf16 for speed.
# Downstream fp32 ops auto-upcast the bf16 features.
with torch.amp.autocast("cuda", dtype=torch.bfloat16):
features = self.backbone.get_intermediate_layers(
(image - self._mean) / self._std,
n=self.layer_indices,
return_class_token=True,
)
basic_feat = self.basic_encoder(2.0 * image - 1.0)
return features, basic_feat, patch_h, patch_w
@dataclass
class EncoderInfiniSplatCfg:
name: Literal["infinisplat"]
sample_point_num: int
image_basic_dim: int
image_backbone_type: str
implicit_gs_query_batch_size: int
implicit_gs_hidden_list: list[int]
gaussian_decoder: GaussianDecoderCfg
class EncoderInfiniSplat(Encoder[EncoderInfiniSplatCfg]):
def __init__(self, cfg: EncoderInfiniSplatCfg) -> None:
super().__init__(cfg)
self.depth_predictor = DepthPro()
self.depth_predictor.eval()
self.image_feature_branch = DinoBasicImageFeatureBranch(
backbone_type=cfg.image_backbone_type,
basic_dim=cfg.image_basic_dim,
)
self.implicit_gs_head = ImplicitGSHead(
hidden_dim=self.image_feature_branch.hidden_dim,
basic_dim=cfg.image_basic_dim,
hidden_list=list(cfg.implicit_gs_hidden_list),
)
self.gaussian_decoder = GaussianDecoder(cfg=cfg.gaussian_decoder)
def _sample_map(
self,
feature_map: torch.Tensor,
coords_yx: torch.Tensor,
) -> torch.Tensor:
sampled = F.grid_sample(
feature_map,
coords_yx.flip(-1).unsqueeze(1),
mode="bilinear",
align_corners=False,
)
return sampled[:, :, 0, :].transpose(1, 2)
def _sample_sparse_coords(
self,
dense_depthmap_flat: torch.Tensor,
intrinsics_flat: torch.Tensor,
image_flat: torch.Tensor,
) -> SparseSamplingOutput:
sample_coords_yx_ndc = []
sample_kind = []
sample_responsibility_area_metric = []
for sample_index, (depth_hw, intrinsic, image_chw) in enumerate(zip(
dense_depthmap_flat[:, 0],
intrinsics_flat,
image_flat,
)):
try:
sampling_output = make_sparse_surface_samples(
depth_hw=depth_hw,
image_chw=image_chw.detach(),
fx=float(intrinsic[0, 0].item()),
fy=float(intrinsic[1, 1].item()),
cx=float(intrinsic[0, 2].item()),
cy=float(intrinsic[1, 2].item()),
sample_point_num=int(self.cfg.sample_point_num),
)
except (RuntimeError, ValueError) as exc:
valid = depth_hw[torch.isfinite(depth_hw) & (depth_hw > 0.0)]
if valid.numel() == 0:
depth_stats = "no positive finite depth"
else:
depth_stats = (
f"valid={int(valid.numel())}/{int(depth_hw.numel())}, "
f"min={float(valid.min().item()):.6g}, "
f"median={float(valid.median().item()):.6g}, "
f"max={float(valid.max().item()):.6g}"
)
raise RuntimeError(
"Surface sampling failed for context sample "
f"flat_index={sample_index}; "
f"{depth_stats}. Original error: {exc}"
) from exc
sample_coords_yx_ndc.append(sampling_output.coords_yx_ndc)
sample_kind.append(sampling_output.sample_kind)
sample_responsibility_area_metric.append(
sampling_output.sample_responsibility_area_metric
)
return SparseSamplingOutput(
coords_yx_ndc=torch.stack(sample_coords_yx_ndc, dim=0),
sample_responsibility_area_metric=torch.stack(sample_responsibility_area_metric, dim=0),
sample_kind=torch.stack(sample_kind, dim=0),
)
def _decode_dino_gaussian_delta(
self,
features,
basic_feat: torch.Tensor,
patch_h: int,
patch_w: int,
coords_yx: torch.Tensor,
) -> torch.Tensor:
feat_map = self.implicit_gs_head.encode_feat(features, patch_h, patch_w)
query_batch_size = int(self.cfg.implicit_gs_query_batch_size)
num_queries = coords_yx.shape[1]
chunks = []
for start in range(0, num_queries, query_batch_size):
end = min(start + query_batch_size, num_queries)
chunks.append(
self.implicit_gs_head.decode_dpt(
feat_map,
basic_feat,
coords_yx[:, start:end],
)
)
return torch.cat(chunks, dim=1)
def forward(
self,
context: BatchedViews,
):
b, v, _, h, w = context["image"].shape
image_flat = rearrange(context["image"], "b v c h w -> (b v) c h w")
intrinsics_norm_flat = rearrange(context["intrinsics"], "b v i j -> (b v) i j")
intrinsics = context["intrinsics"].clone()
intrinsics[:, :, 0] = intrinsics[:, :, 0] * w
intrinsics[:, :, 1] = intrinsics[:, :, 1] * h
intrinsics_flat = rearrange(intrinsics, "b v i j -> (b v) i j")
with torch.no_grad():
self.depth_predictor.eval()
dense_depthmap_flat = self.depth_predictor(
{
"image": image_flat,
"intrinsics": intrinsics_norm_flat,
}
)
if dense_depthmap_flat.ndim != 4 or dense_depthmap_flat.shape[1] != 1:
raise AssertionError(
"InfiniSplat expects the selected depth model to return a single dense depth layer."
)
sampling_output_flat = self._sample_sparse_coords(
dense_depthmap_flat=dense_depthmap_flat.detach(),
intrinsics_flat=intrinsics_flat,
image_flat=image_flat,
)
sample_coords_yx_ndc_flat = sampling_output_flat.coords_yx_ndc
sample_kind_flat = sampling_output_flat.sample_kind
sample_responsibility_area_metric_flat = (
sampling_output_flat.sample_responsibility_area_metric
)
sample_depths_flat = self._sample_map(dense_depthmap_flat, sample_coords_yx_ndc_flat)
sampled_rgb_flat = self._sample_map(image_flat, sample_coords_yx_ndc_flat)
features, basic_feat, patch_h, patch_w = self.image_feature_branch(image_flat)
gaussian_delta_flat = self._decode_dino_gaussian_delta(
features=features,
basic_feat=basic_feat,
patch_h=patch_h,
patch_w=patch_w,
coords_yx=sample_coords_yx_ndc_flat,
)
sample_depths = rearrange(sample_depths_flat, "(b v) n c -> b v n c", b=b, v=v)
sample_coords_yx_ndc = rearrange(
sample_coords_yx_ndc_flat,
"(b v) n c -> b v n c",
b=b,
v=v,
)
sample_responsibility_area_metric = rearrange(
sample_responsibility_area_metric_flat,
"(b v) n -> b v n",
b=b,
v=v,
)
sample_kind = rearrange(sample_kind_flat, "(b v) n -> b v n", b=b, v=v)
sampled_rgb = rearrange(sampled_rgb_flat, "(b v) n c -> b v n c", b=b, v=v)
gaussian_delta = rearrange(gaussian_delta_flat, "(b v) n c -> b v n c", b=b, v=v)
gaussians_ndc: Gaussians3D = self.gaussian_decoder(
delta=gaussian_delta,
coords_yx_ndc=sample_coords_yx_ndc,
depths=sample_depths,
rgb=sampled_rgb,
intrinsics=intrinsics,
sample_kind=sample_kind,
sample_responsibility_area_metric=sample_responsibility_area_metric,
image_shape=(h, w),
)
gaussians: Gaussians3D = unproject_gaussians(
gaussians_ndc,
context["extrinsics"],
intrinsics,
(w, h),
)
return {"gaussians": gaussians}
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