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README.md

@@ -25,23 +25,34 @@ Below is a collage showing sample RGB inputs from the constituent datasets:
 
 ## Component Datasets
 
-Detailed breakdown of the included subsets:
+This collection aggregates four datasets converted to the standard BOP format to facilitate comparative evaluation.
 
 ### 1. ClearPose
-A large-scale dataset centered on transparent objects, providing high-quality transparent object meshes and real/synthetic RGB-D images. It is a key benchmark for transparent object pose estimation.
-*   **Source**: [ClearPose Project](https://progress.eecs.umich.edu/projects/clearpose/)
+**Source**: [ClearPose Project](https://progress.eecs.umich.edu/projects/clearpose/)
+ClearPose consists of **63 transparent or opaque household objects** captured in various lighting conditions and occlusions (e.g., glass utensils on a tabletop).
+*   **Note**: This dataset includes the downsampled version (every 100th image from each scene) to account for the small pose differences between consecutive frames in continuous motion and to decrease inference time.
 
 ### 2. DIMO (Dataset of Industrial Metal Objects)
-Focuses on symmetric, textureless, and highly reflective industrial metal objects. It includes over 30,000 real-world images and over 500,000 synthetic images, specifically designed to challenge vision systems with difficult material properties.
-*   **Source**: [DIMO Project](https://pderoovere.github.io/dimo/)
+**Source**: [DIMO Project](https://pderoovere.github.io/dimo/)
+DIMO consists of **six reflective metallic parts** (colored, shiny, and matte finish) on a metallic surface. These objects exhibit a shiny appearance, designed to challenge rendering and pose estimation methods that must model view-dependent effects.
 
 ### 3. HouseCat6D
-A large-scale multi-modal category-level 6D object perception dataset featuring diverse household objects in realistic scenarios. It includes high-quality camera trajectories and dense grasp annotations, covering objects with varying photometric complexity, including some transparency and reflections.
-*   **Source**: [HouseCat6D Project](https://sites.google.com/view/housecat6d)
+**Source**: [HouseCat6D Project](https://sites.google.com/view/housecat6d)
+A large-scale multi-modal dataset consisting of **textured, shiny, metallic, and matte objects** of different categories in realistic scenarios. It serves as a comprehensive benchmark for diverse household objects.
 
 ### 4. TRansPose
-A large-scale multispectral dataset dedicated to transparent objects, facilitating research into how different spectral data can aid in the perception of transparent surfaces.
-*   **Source**: [TRansPose Project](https://sites.google.com/view/transpose-dataset)
+**Source**: [TRansPose Project](https://sites.google.com/view/transpose-dataset)
+TRansPose consists of **99 transparent objects** (glassy and plastic objects with different optical properties) cluttered on a tabletop.
+*   **Note**: Similar to ClearPose, this dataset is downsampled (every 10th image) from the original continuous motion capture. While the original setup included RGB, RGBD, and TIR images, this distribution relies on RGB images and bounding boxes, as the method requires only RGB and 2D detection.
+
+## Visualization
+Randomly selected images from these datasets can be seen below:
+*   **(a) ClearPose**: Glass utensils on a tabletop.
+*   **(b) DIMO**: Shiny metallic parts.
+*   **(c) HouseCat6D**: Diverse textured/matte objects.
+*   **(d) TRansPose**: Cluttered glassy/plastic objects.
+
+![Dataset Collage](dataset_collage.png)
 
 ## Dataset Structure
 
@@ -59,8 +70,8 @@ Each sub-dataset follows the BOP directory structure:
 ```text
 dataset/
 ├── ClearPose/
-│   ├── models/                 # Object models (.ply, .obj, .mtl, models_info.json)
-│   ├── nerfs/                  # NeRF data (where applicable)
+│   ├── models/                 # Object models (.ply, models_info.json)
+│   ├── nerfs/                  # NeRF data (Instant NGP format)
 │   ├── test/                   # Test scenes
 │   │   ├── 001001/             # Scene ID
 │   │   │   ├── scene_camera.json # Camera parameters