# UnCommon Objects in 3D

arXiv: https://arxiv.org/abs/2501.07574 · HTML: https://mlsanity.com/paper/2501.07574/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 60 of its 75 references that are in the map.
- First posted on arXiv 13 Jan 2025.

## Builds on (most cited first, 30 of 60)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [CLIPScore: A Reference-free Evaluation Metric for Image Captioning](https://mlsanity.com/paper/2104.08718/) (2021)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://mlsanity.com/paper/2206.00927/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://mlsanity.com/paper/1908.03195/) (2019)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [LRM: Large Reconstruction Model for Single Image to 3D](https://mlsanity.com/paper/2311.04400/) (2023)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation](https://mlsanity.com/paper/2402.05054/) (2024)
- [IBRNet: Learning Multi-View Image-Based Rendering](https://mlsanity.com/paper/2102.13090/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction](https://mlsanity.com/paper/2109.00512/) (2021)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models](https://mlsanity.com/paper/2404.07191/) (2024)
- [CAT3D: Create Anything in 3D with Multi-View Diffusion Models](https://mlsanity.com/paper/2405.10314/) (2024)

## Built on by (most cited first, 17 of 17)

- [Uni3C: Unifying Precisely 3D-Enhanced Camera and Human Motion Controls for Video Generation](https://mlsanity.com/paper/2504.14899/) (2025)
- [Simulating the Real World: A Unified Survey of Multimodal Generative Models](https://mlsanity.com/paper/2503.04641/) (2025)
- [SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting](https://mlsanity.com/paper/2506.08710/) (2025)
- [Tinker: Diffusion's Gift to 3D--Multi-View Consistent Editing From Sparse Inputs without Per-Scene Optimization](https://mlsanity.com/paper/2508.14811/) (2025)
- [Scaling Sequence-to-Sequence Generative Neural Rendering](https://mlsanity.com/paper/2510.04236/) (2025)
- [NymeriaPlus: Enriching Nymeria Dataset with Additional Annotations and Data](https://mlsanity.com/paper/2603.18496/) (2026)
- [Droplet3D: Commonsense Priors from Videos Facilitate 3D Generation](https://mlsanity.com/paper/2508.20470/) (2025)
- [ReLi3D: Relightable Multi-view 3D Reconstruction with Disentangled Illumination](https://mlsanity.com/paper/2603.19753/) (2026)
- [Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering](https://mlsanity.com/paper/2604.27590/) (2026)
- [Audio-Visual Camera Pose Estimation with Passive Scene Sounds and In-the-Wild Video](https://mlsanity.com/paper/2512.12165/) (2025)
- [MV-SAM: Multi-view Promptable Segmentation using Pointmap Guidance](https://mlsanity.com/paper/2601.17866/) (2026)
- [Every9D-21M: Large-Scale Real-World 9D Canonicalization of Everyday Objects](https://mlsanity.com/paper/2605.28270/) (2026)
- [RayDer: Scalable Self-Supervised Novel View Synthesis from Real-World Video](https://mlsanity.com/paper/2605.31535/) (2026)
- [Eliciting Complex Spatial Reasoning in MLLMs through Wide-Baseline Matching](https://mlsanity.com/paper/2606.03577/) (2026)
- [RefGC-SR2: Reference-guided Super-Resolution and Refinement of AI Generated Content](https://mlsanity.com/paper/2606.15158/) (2026)
- [Emergence of a Shared Canonical Object Frame from In-the-Wild Videos](https://mlsanity.com/paper/2606.30058/) (2026)
- [GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model](https://mlsanity.com/paper/2609.19716/) (2026)

## Abstract (arXiv)

We introduce Uncommon Objects in 3D (uCO3D), a new object-centric dataset for 3D deep learning and 3D generative AI. uCO3D is the largest publicly-available collection of high-resolution videos of objects with 3D annotations that ensures full-360circ coverage. uCO3D is significantly more diverse than MVImgNet and CO3Dv2, covering more than 1,000 object categories. It is also of higher quality, due to extensive quality checks of both the collected videos and the 3D annotations. Similar to analogous datasets, uCO3D contains annotations for 3D camera poses, depth maps and sparse point clouds. In addition, each object is equipped with a caption and a 3D Gaussian Splat reconstruction. We train several large 3D models on MVImgNet, CO3Dv2, and uCO3D and obtain superior results using the latter, showing that uCO3D is better for learning applications.