# TRansPose: Large-Scale Multispectral Dataset for Transparent Object

arXiv: https://arxiv.org/abs/2307.05016 · HTML: https://mlsanity.com/paper/2307.05016/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 39 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 21 of 21)

- [Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching](https://mlsanity.com/paper/1710.01330/) (2017)
- [ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation](https://mlsanity.com/paper/1910.02550/) (2019)
- [Dex-NeRF: Using a Neural Radiance Field to Grasp Transparent Objects](https://mlsanity.com/paper/2110.14217/) (2021)
- [Segmenting Transparent Objects in the Wild](https://mlsanity.com/paper/2003.13948/) (2020)
- [KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects](https://mlsanity.com/paper/1912.02805/) (2019)
- [TransCG: A Large-Scale Real-World Dataset for Transparent Object Depth Completion and a Grasping Baseline](https://mlsanity.com/paper/2202.08471/) (2022)
- [Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects](https://mlsanity.com/paper/2208.03792/) (2022)
- [ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes](https://mlsanity.com/paper/2106.02740/) (2021)
- [ClearPose: Large-scale Transparent Object Dataset and Benchmark](https://mlsanity.com/paper/2203.03890/) (2022)
- [PhoCaL: A Multi-Modal Dataset for Category-Level Object Pose Estimation with Photometrically Challenging Objects](https://mlsanity.com/paper/2205.08811/) (2022)
- [StereOBJ-1M: Large-scale Stereo Image Dataset for 6D Object Pose Estimation](https://mlsanity.com/paper/2109.10115/) (2021)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [TACO: Trash Annotations in Context for Litter Detection](https://mlsanity.com/paper/2003.06975/) (2020)
- [TOM-Net: Learning Transparent Object Matting from a Single Image](https://mlsanity.com/paper/1803.04636/) (2018)
- [TransCut: Transparent Object Segmentation from a Light-Field Image](https://mlsanity.com/paper/1511.06853/) (2015)
- [Glass Segmentation with RGB-Thermal Image Pairs](https://mlsanity.com/paper/2204.05453/) (2022)
- [A4T: Hierarchical Affordance Detection for Transparent Objects Depth Reconstruction and Manipulation](https://mlsanity.com/paper/2207.04907/) (2022)
- [LIT: Light-field Inference of Transparency for Refractive Object Localization](https://mlsanity.com/paper/1910.00721/) (2019)
- [ProgressLabeller: Visual Data Stream Annotation for Training Object-Centric 3D Perception](https://mlsanity.com/paper/2203.00283/) (2022)
- [Robotic Perception of Object Properties using Tactile Sensing](https://mlsanity.com/paper/2112.14119/) (2021)
- [Where Shall I Touch? Vision-Guided Tactile Poking for Transparent Object Grasping](https://mlsanity.com/paper/2208.09743/) (2022)

## Built on by (most cited first, 15 of 15)

- [A Survey of Embodied Learning for Object-Centric Robotic Manipulation](https://mlsanity.com/paper/2408.11537/) (2024)
- [TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation](https://mlsanity.com/paper/2502.07840/) (2025)
- [Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation](https://mlsanity.com/paper/2512.23705/) (2025)
- [ClearDepth: Enhanced Stereo Perception of Transparent Objects for Robotic Manipulation](https://mlsanity.com/paper/2409.08926/) (2024)
- [Fieldscale: Locality-Aware Field-based Adaptive Rescaling for Thermal Infrared Image](https://mlsanity.com/paper/2405.15395/) (2024)
- [SpectralWaste Dataset: Multimodal Data for Waste Sorting Automation](https://mlsanity.com/paper/2403.18033/) (2024)
- [Safe-Night VLA: Seeing the Unseen via Thermal-Perceptive Vision-Language-Action Models for Safety-Critical Manipulation](https://mlsanity.com/paper/2603.05754/) (2026)
- [TRAN-D: 2D Gaussian Splatting-based Sparse-view Transparent Object Depth Reconstruction via Physics Simulation for Scene Update](https://mlsanity.com/paper/2507.11069/) (2025)
- [DeLTa: Demonstration and Language-Guided Novel Transparent Object Manipulation](https://mlsanity.com/paper/2510.05662/) (2025)
- [TransNormal: Dense Visual Semantics for Diffusion-based Transparent Object Normal Estimation](https://mlsanity.com/paper/2602.00839/) (2026)
- [Object Pose Estimation Using Implicit Representation For Transparent Objects](https://mlsanity.com/paper/2410.13465/) (2024)
- [Power Battery Detection](https://mlsanity.com/paper/2508.07797/) (2025)
- [VAIR: Visuo-Acoustic Implicit Representations for Low-Cost, Multi-Modal Transparent Surface Reconstruction in Indoor Scenes](https://mlsanity.com/paper/2411.04963/) (2024)
- [TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation](https://mlsanity.com/paper/2609.06665/) (2026)
- [GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion](https://mlsanity.com/paper/2609.36844/) (2026)

## Abstract (arXiv)

Transparent objects are encountered frequently in our daily lives, yet recognizing them poses challenges for conventional vision sensors due to their unique material properties, not being well perceived from RGB or depth cameras. Overcoming this limitation, thermal infrared cameras have emerged as a solution, offering improved visibility and shape information for transparent objects. In this paper, we present TRansPose, the first large-scale multispectral dataset that combines stereo RGB-D, thermal infrared (TIR) images, and object poses to promote transparent object research. The dataset includes 99 transparent objects, encompassing 43 household items, 27 recyclable trashes, 29 chemical laboratory equivalents, and 12 non-transparent objects. It comprises a vast collection of 333,819 images and 4,000,056 annotations, providing instance-level segmentation masks, ground-truth poses, and completed depth information. The data was acquired using a FLIR A65 thermal infrared (TIR) camera, two Intel RealSense L515 RGB-D cameras, and a Franka Emika Panda robot manipulator. Spanning 87 sequences, TRansPose covers various challenging real-life scenarios, including objects filled with water, diverse lighting conditions, heavy clutter, non-transparent or translucent containers, objects in plastic bags, and multi-stacked objects. TRansPose dataset can be accessed from the following link: https://sites.google.com/view/transpose-dataset