jacoblambert · Oct 22, 2026 · Oct 13, 2026 · Oct 13, 2026 · Oct 13, 2026 · Oct 13, 2026
diff --git a/docs/cli/t4viz.md b/docs/cli/t4viz.md
@@ -58,7 +58,7 @@ t4viz instance <DATA_ROOT> <INSTANCE_TOKEN1> <INSTANCE_TOKEN2> ... [OPTIONS]
 
 This command performs the same behavior with [`T4Devkit.render_pointcloud(...)`](../tutorials/render.md#rendering-pointcloud).
 
-If a pointcloud `sample_data` record has `info_filename`, `t4viz pointcloud` loads the metainfo JSON automatically and uses `num_pts_feats` to parse extended point layouts. Without that file, it falls back to the standard 5-feature layout.
+The point layout comes from the file (Parquet, BIN, PCD) or from `sensor.lidar.fields` (`.bin`; for 0.x datasets it is taken from the `.info` files when loading).
 
 For options, run `t4viz pointcloud -h`.
 

diff --git a/docs/tutorials/customize.md b/docs/tutorials/customize.md
@@ -57,8 +57,8 @@ from __future__ import annotations
 
 from attrs import define, field
 
-from t4_devkit.schema import SCHEMAS, SchemaName, SchemaBase
-from t4_devkit.common import load_json
+from t4_devkit.schema import SchemaBase
+from t4_devkit.schema.v1 import SCHEMAS, SchemaName
 
 __all__ = ["CustomAttribute"]
 
@@ -77,6 +77,8 @@ class CustomAttribute(SchemaBase):
     description: str | None = field(default=None)
 ```
 
+`T4Devkit` builds every table with the T4Dataset 1.0 registry (`t4_devkit.schema.v1.SCHEMAS`), for 1.0 datasets and for 0.x datasets (converted to 1.0 records when loading), so a custom class receives 1.0 records.
+
 Note that `CustomAttribute` should be imported before instantiating `T4Devkit` class.
 Then modify `__init__.py` in order to import it automatically:
 

diff --git a/docs/tutorials/initialize.md b/docs/tutorials/initialize.md
@@ -47,30 +47,33 @@ You can initialize a `T4Devkit` instance as follows:
 
 >>> t4 = T4Devkit("data/tier4/", verbose=True)
 ======
-Loading T4 tables in `annotation`...
-Reverse indexing...
-Done reverse indexing in 0.007 seconds.
-======
-13 attribute
-7 calibrated_sensor
-8 category
-2524 ego_pose
-106 instance
-1 log
-1 map
+Loading T4 tables...
 72 sample
-2390 sample_annotation
 2524 sample_data
-4 visibility
+2524 ego_pose
 7 sensor
 1 scene
+1 log
+1 vehicle
+1 map
+0 vehicle_state
+8 category
+13 attribute
+4 visibility
+106 instance
+2390 sample_annotation
 1326 object_ann
-0 surface_ann
 0 keypoint
+0 traffic_light
+0 route
+0 scenario
+0 caption
 Done loading in 0.061 seconds.
 ======
 ```
 
+Both T4Dataset 1.0 datasets (with `manifest.json`) and 0.x datasets load, as 1.0 records: 0.x tables are converted when loading (see [Loading 0.x Datasets](../schema/v1/index.md#loading-0x-datasets)).
+
 Note that if you doesn't specify `revision` parameter in construction, it searches the latest version of the dataset.
 By specifying `revision=<VERSION>`, you can load the specific version of the dataset.
 
@@ -90,7 +93,7 @@ By specifying `revision=<VERSION>`, you can load the specific version of the dat
 
 ### `sample`
 
-`sample` is an annotated keyframe of a scene at a given timestamp.
+`sample` is one frame of a scene: the observations of all channels at that moment. Every frame is a sample; `sample.has_key_frame` marks the annotated ones (key frames), which carry the labels.
 
 ```python
 >>> first_sample_token = my_scene.first_sample_token
@@ -184,22 +187,12 @@ An `attribute` is a property of an instance that may change throughout different
 ### `sensor`
 
 T4 dataset consists of several type of sensors.
-The supported sensor modalities and channels are defined in `t4_devkit/schema/tables/sensor.py`.
+Each `sensor` row is one sensor with its calibration: `translation` and `rotation` with respect to the ego vehicle body frame, and a block for its modality (`camera` with intrinsics and image size, or `lidar` with its point fields).
 
 ```python
 >>> t4.sensor
 ```
 
-### `calibrated_sensor`
-
-`calibrated_sensor` consists of the definition of a calibration of a particular sensor based on a vehicle.
-
-```python
->>> t4.calibrated_sensor
-```
-
-Note that the `translation` and `rotation` parameters are given with respect to the ego vehicle body frame.
-
 ### `ego_pose`
 
 `ego_pose` contains information about the `translation` and `rotation` of the ego vehicle, with respect to the global coordinate system.

diff --git a/docs/tutorials/render.md b/docs/tutorials/render.md
@@ -36,7 +36,9 @@ If you want to visualize annotation results, `T4Devkit` supports some rendering
 >>> t4.render_pointcloud()
 ```
 
-If `SampleData.info_filename` points to a pointcloud metainfo JSON file, `T4Devkit.render_pointcloud()` loads it automatically. This allows rendering pointclouds with extended per-point features such as `return_type` or `timestamp`. When no metainfo file is available, the standard 5-feature layout is assumed.
+Frames are read by their own format: point layouts come from the file (Parquet, PCD) or from `sensor.lidar.fields` (`.bin`; for 0.x datasets it is taken from the `.info` files when loading), and camera frames from per-frame images or from the channel's AV1 video (`sample_data.frame_index`).
+
+0.x LiDAR segmentation (`lidarseg`) is not rendered by `T4Devkit`.
 
 ![Render PointCloud GIF](../assets/render_pointcloud.gif)
 
@@ -129,11 +131,11 @@ from t4_devkit.dataclass import LidarPointCloud
 from t4_devkit.viewer import PointCloudColorMode
 # Point cloud channel name
 >>> lidar_channel = "LIDAR_TOP"
-# Load point cloud from file.
-# Pass metainfo_filepath when the binary stores more than 5 float32 values per point.
+# Load point cloud from file: one frame of a Parquet file, or a .pcd / .bin file.
+# A .bin file has no header: pass its layout, e.g. from `sensor.lidar.fields`.
 >>> pointcloud = LidarPointCloud.from_file(
-...     <PATH_TO_POINTCLOUD.pcd.bin>,
-...     metainfo_filepath=<PATH_TO_POINTCLOUD_INFO.json>,
+...     <PATH_TO_LIDAR.parquet>,
+...     frame_index=<FRAME_INDEX>,
 ... )
 >>> color_mode = PointCloudColorMode.DISTANCE
 >>> viewer.render_pointcloud(seconds, lidar_channel, pointcloud, color_mode)