# Multiview RGB-D Dataset for Object Instance Detection

arXiv: https://arxiv.org/abs/1609.07826 · HTML: https://mlsanity.com/paper/1609.07826/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2016.

## Built on by (most cited first, 26 of 26)

- [Ego4D: Around the World in 3,000 Hours of Egocentric Video](https://mlsanity.com/paper/2110.07058/) (2021)
- [Class-agnostic Object Detection with Multi-modal Transformer](https://mlsanity.com/paper/2111.11430/) (2021)
- [Making Images Real Again: A Comprehensive Survey on Deep Image Composition](https://mlsanity.com/paper/2106.14490/) (2021)
- [GeometryCrafter: Consistent Geometry Estimation for Open-world Videos with Diffusion Priors](https://mlsanity.com/paper/2504.01016/) (2025)
- [GAIA: A Transfer Learning System of Object Detection that Fits Your Needs](https://mlsanity.com/paper/2106.11346/) (2021)
- [Detection Hub: Unifying Object Detection Datasets via Query Adaptation on Language Embedding](https://mlsanity.com/paper/2206.03484/) (2022)
- [DAMEX: Dataset-aware Mixture-of-Experts for visual understanding of mixture-of-datasets](https://mlsanity.com/paper/2311.04894/) (2023)
- [Continual Object Detection via Prototypical Task Correlation Guided Gating Mechanism](https://mlsanity.com/paper/2205.03055/) (2022)
- [DALL-E for Detection: Language-driven Compositional Image Synthesis for Object Detection](https://mlsanity.com/paper/2206.09592/) (2022)
- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [Instance Tracking in 3D Scenes from Egocentric Videos](https://mlsanity.com/paper/2312.04117/) (2023)
- [Track4World: Feedforward World-centric Dense 3D Tracking of All Pixels](https://mlsanity.com/paper/2603.02573/) (2026)
- [A Survey on RGB-D Datasets](https://mlsanity.com/paper/2201.05761/) (2022)
- [TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes](https://mlsanity.com/paper/2203.09440/) (2022)
- [Beyond Generation: Harnessing Text to Image Models for Object Detection and Segmentation](https://mlsanity.com/paper/2309.05956/) (2023)
- [Learning with Free Object Segments for Long-Tailed Instance Segmentation](https://mlsanity.com/paper/2202.11124/) (2022)
- [Semi-supervised Viewpoint Estimation with Geometry-aware Conditional Generation](https://mlsanity.com/paper/2104.01103/) (2021)
- [RICO: Two Realistic Benchmarks and an In-Depth Analysis for Incremental Learning in Object Detection](https://mlsanity.com/paper/2508.13878/) (2025)
- [A High-Resolution Dataset for Instance Detection with Multi-View Instance Capture](https://mlsanity.com/paper/2310.19257/) (2023)
- [Self-Annotated 3D Geometric Learning for Smeared Points Removal](https://mlsanity.com/paper/2311.09029/) (2023)
- [DAGE: Dual-Stream Architecture for Efficient and Fine-Grained Geometry Estimation](https://mlsanity.com/paper/2603.03744/) (2026)
- [DepthSync: Diffusion Guidance-Based Depth Synchronization for Scale- and Geometry-Consistent Video Depth Estimation](https://mlsanity.com/paper/2507.01603/) (2025)
- [DeePaste -- Inpainting for Pasting](https://mlsanity.com/paper/2112.10600/) (2021)
- [Gram-SLD: Automatic Self-labeling and Detection for Instance Objects](https://mlsanity.com/paper/2112.03641/) (2021)
- [A Novel Algorithm for Exact Concave Hull Extraction](https://mlsanity.com/paper/2206.11481/) (2022)
- [Object Tracking in a 360o View: A Novel Perspective on Bridging the Gap to Biomedical Advancements](https://mlsanity.com/paper/2412.01119/) (2024)

## Abstract (arXiv)

This paper presents a new multi-view RGB-D dataset of nine kitchen scenes, each containing several objects in realistic cluttered environments including a subset of objects from the BigBird dataset. The viewpoints of the scenes are densely sampled and objects in the scenes are annotated with bounding boxes and in the 3D point cloud. Also, an approach for detection and recognition is presented, which is comprised of two parts: i) a new multi-view 3D proposal generation method and ii) the development of several recognition baselines using AlexNet to score our proposals, which is trained either on crops of the dataset or on synthetically composited training images. Finally, we compare the performance of the object proposals and a detection baseline to the Washington RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset is more challenging for object detection and recognition. The dataset is available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.