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README.md

@@ -2,14 +2,15 @@
 license: cc-by-4.0
 pretty_name: Single Tree High-Density Photogrammetry Dataset
 task_categories: [image-to-3d, depth-estimation]
-tags: [photogrammetry, 3d-reconstruction, structure-from-motion, gaussian-splatting, point-cloud, drone, uav, aerial-imagery, vegetation, forestry]
+tags: [photogrammetry, 3d-reconstruction, structure-from-motion, gaussian-splatting, 3dgs, nerf, radiance-fields, colmap, point-cloud, drone, uav, aerial-imagery, vegetation, forestry, camera-poses]
 size_categories: [n<1K]
 ---
 
 # Single Tree — High-Density Photogrammetry Dataset
 
 **812 photos of one mature deciduous tree, flown from 0.5 to 7.9 m above the ground. 14.1 GB.
-807 align, camera poses included.** CC BY 4.0.
+807 align, and the solved camera poses ship with it — ready for Gaussian splatting without
+running COLMAP first.** CC BY 4.0.
 
 > ## 🏆 Winner — RealityCapture #RCmonthlyChallenge, August 2020
 >
@@ -99,7 +100,8 @@ only.
 
 [`poses/`](poses/) has 807 solved cameras with intrinsics and extrinsics, plus a
 1,206,765-point sparse cloud. Same thing COLMAP would give you, so you can skip that step and
-go straight to splatting or meshing.
+go straight to **Gaussian splatting (3DGS)**, NeRF, or meshing — poses plus tie points is
+exactly what those pipelines ingest.
 
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