README: paper title in citation, GitHub link, drop em-dashes

README.md

@@ -19,6 +19,8 @@ clips. Used as the data backend for the
 [EgoInfinity Browser](https://huggingface.co/spaces/Rice-RobotPI-Lab/egoinfinity)
 Space.
 
+Source code: <https://github.com/Rice-RobotPI-Lab/EgoInfinity>
+
 [Action100M]: https://github.com/facebookresearch/Action100M
 
 ## Contents
@@ -27,19 +29,70 @@ Space.
 samples/
 ├── index.json                 # browse-time episode list (consumed by the Space)
 └── <clip_id>/
-    ├── scene.json             # camera intrinsics, object metadata, durations
-    ├── signals.json           # per-frame action signals (timeseries)
+    ├── scene.json             # camera intrinsics, object metadata, asset paths
+    ├── signals.json           # per-frame action signals (OR-merged across objects)
     ├── thumb.jpg              # 320×180 preview rendered from depth
-    ├── depth.mp4              # MoGe-2 depth, inferno colormap (854×480)
-    ├── flow.mp4               # MEMFOF optical flow visualization
-    ├── mask.mp4               # SAM-tracked object mask cutout
     ├── recording.viser        # full 3D scene (point cloud + meshes + hands)
-    ├── hand_joints.bin        # (T, H, 21, 3) float32 — 3D joint positions
-    ├── hand_verts.bin         # (T, H, 778, 3) float32 — baked MANO vertices
-    ├── hand_faces.bin         # (F, 3) uint16 — MANO topology
-    └── hand_meta.json         # bone connectivity + helper metadata
+    │
+    │  # Visualization (lossy, fast for streaming)
+    ├── depth.mp4              # MoGe-2 depth, inferno colormap
+    ├── flow.mp4               # MEMFOF optical flow visualization
+    ├── mask.mp4               # SAM-tracked object cutout × original RGB
+    │
+    │  # Hand reconstruction (lossless)
+    ├── hand_joints.bin        # (T, H, 21, 3) float32; 3D joint positions
+    ├── hand_verts.bin         # (T, H, 778, 3) float32; baked MANO vertices
+    ├── hand_faces.bin         # (F, 3) uint16; MANO topology
+    ├── hand_meta.json         # bone connectivity + helper metadata
+    │
+    │  # Object reconstruction (lossless)
+    ├── object_pose.bin        # (T, N_obj, 4, 4) float32; per-frame 6DoF
+    ├── object_obb.bin         # (N_obj, 8, 3) float32; first-valid-frame OBB
+    ├── objects/obj_N.ply      # SAM3D point cloud per object
+    │
+    │  # Raw arrays (lossless, downstream-ready)
+    ├── depth.npz              # (T, H, W) uint16 mm; lossless depth
+    ├── masks.npz              # per-object packed-bit SAM masks
+    ├── bg_template.png        # uint16-mm PNG; bg depth template
+    └── pose_track.json        # full per-object tracker timeseries
+```
+
+## Loading raw arrays
+
+```python
+import numpy as np, cv2, json
+
+# Depth (uint16 mm → meters). Sentinel 0 = absent / NaN.
+depth = np.load("depth.npz")["depth"]                # (T, H, W) uint16
+depth_m = depth.astype(np.float32) / 1000.0
+
+# Per-object SAM masks (packed bits per frame per object).
+m = np.load("masks.npz")
+T, H, W = m["_shape"]
+oids = m["_oids"]                                    # ordered object ids
+def mask_for(oid: int, t: int) -> np.ndarray:
+    bits = np.unpackbits(m[f"oid_{oid}"][t])[: H * W]
+    return bits.reshape(H, W).astype(bool)
+
+# Background depth template (rest scene) → meters
+bg = cv2.imread("bg_template.png", cv2.IMREAD_UNCHANGED).astype(np.float32) / 1000.0
+
+# Per-object tracker state: contact_soft, grasp_soft, motion, trust, chamfer,
+# scale_correction, obs_obb_per_frame, etc. Keyed by str(oid).
+pti = json.load(open("pose_track.json"))
+
+# Per-frame 6DoF object pose (camera frame), (T, N_obj, 4, 4) float32
+N_obj = len(json.load(open("scene.json"))["reconstruction"]["objects"])
+poses = np.fromfile("object_pose.bin", dtype=np.float32).reshape(-1, N_obj, 4, 4)
 ```
 
+> **Note:** original RGB frames are not redistributed. Anything that needs
+> the source pixels (re-running SAM3 detect, SAM2 track, MEMFOF flow, or
+> SAM3D mesh build) cannot be done from this dataset alone. Algorithms that
+> consume `(depth, masks, hand_*, mesh, pose, bg_template)` (grasp / contact
+> classification, state-machine tuning, ICP-based pose refinement) work
+> standalone.
+
 `<clip_id>` is `<youtube_video_id>_<start_sec>_<end_sec>`. The only original
 YouTube pixels that appear in this repository are inside the SAM-tracked
 object region of `mask.mp4` (everything outside the mask is painted black);
@@ -68,7 +121,7 @@ license file.
 
 ```bibtex
 @misc{egoinfinity2026,
-  title  = {EgoInfinity: TBD},
+  title  = {EgoInfinity: A Web-Scale Data Engine for Video-to-Action Robot Learning through Egocentric Views},
   author = {Rice Robot Perception \& Intelligence Lab},
   year   = {2026},
   note   = {Preview release}