# SCRREAM : SCan, Register, REnder And Map:A Framework for Annotating Accurate and Dense 3D Indoor Scenes with a Benchmark arXiv: https://arxiv.org/abs/2410.22715 ยท HTML: https://mlsanity.com/paper/2410.22715/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 43 references that are in the map. - First posted on arXiv 30 Oct 2024. ## Builds on (most cited first, 30 of 30) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017) - [The Replica Dataset: A Digital Replica of Indoor Spaces](https://mlsanity.com/paper/1906.05797/) (2019) - [ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes](https://mlsanity.com/paper/2308.11417/) (2023) - [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023) - [Mip-Splatting: Alias-free 3D Gaussian Splatting](https://mlsanity.com/paper/2311.16493/) (2023) - [Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans](https://mlsanity.com/paper/2012.15838/) (2020) - [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023) - [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021) - [Habitat 2.0: Training Home Assistants to Rearrange their Habitat](https://mlsanity.com/paper/2106.14405/) (2021) - [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019) - [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021) - [ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Data](https://mlsanity.com/paper/2111.08897/) (2021) - [Joint 2D-3D-Semantic Data for Indoor Scene Understanding](https://mlsanity.com/paper/1702.01105/) (2017) - [Neural RGB-D Surface Reconstruction](https://mlsanity.com/paper/2104.04532/) (2021) - [NeuMan: Neural Human Radiance Field from a Single Video](https://mlsanity.com/paper/2203.12575/) (2022) - [RoboTHOR: An Open Simulation-to-Real Embodied AI Platform](https://mlsanity.com/paper/2004.06799/) (2020) - [Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM](https://mlsanity.com/paper/2304.14377/) (2023) - [Panoptic Studio: A Massively Multiview System for Social Interaction Capture](https://mlsanity.com/paper/1612.03153/) (2016) - [HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion](https://mlsanity.com/paper/2305.06356/) (2023) - [Gaussian-SLAM: Photo-realistic Dense SLAM with Gaussian Splatting](https://mlsanity.com/paper/2312.10070/) (2023) - [Combining Implicit Function Learning and Parametric Models for 3D Human Reconstruction](https://mlsanity.com/paper/2007.11432/) (2020) - [LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration](https://mlsanity.com/paper/2010.12447/) (2020) - [ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions](https://mlsanity.com/paper/2401.10232/) (2024) - [HouseCat6D -- A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios](https://mlsanity.com/paper/2212.10428/) (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) - [On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks](https://mlsanity.com/paper/2303.14840/) (2023) ## Built on by (most cited first, 9 of 9) - [Off The Grid: Detection of Primitives for Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2512.15508/) (2025) - [LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning](https://mlsanity.com/paper/2504.18424/) (2025) - [XYZ-IBD: Benchmarking Robust 6D Object Pose Estimation under Real-World Industrial Complexity](https://mlsanity.com/paper/2506.00599/) (2025) - [NOVA3R: Non-pixel-aligned Visual Transformer for Amodal 3D Reconstruction](https://mlsanity.com/paper/2603.04179/) (2026) - [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025) - [DEPTHOR++: Robust Depth Enhancement from a Real-World Lightweight dToF and RGB Guidance](https://mlsanity.com/paper/2509.26498/) (2025) - [UnReflectAnything: RGB-Only Highlight Removal by Rendering Synthetic Specular Supervision](https://mlsanity.com/paper/2512.09583/) (2025) - [Surflo: Consistent 3D Surface Flow Model with Global State](https://mlsanity.com/paper/2606.13644/) (2026) - [VolFill: Single-View Amodal 3D Scene Reconstruction with Volumetric Flow Matching](https://mlsanity.com/paper/2605.31466/) (2026) ## Abstract (arXiv) Traditionally, 3d indoor datasets have generally prioritized scale over ground-truth accuracy in order to obtain improved generalization. However, using these datasets to evaluate dense geometry tasks, such as depth rendering, can be problematic as the meshes of the dataset are often incomplete and may produce wrong ground truth to evaluate the details. In this paper, we propose SCRREAM, a dataset annotation framework that allows annotation of fully dense meshes of objects in the scene and registers camera poses on the real image sequence, which can produce accurate ground truth for both sparse 3D as well as dense 3D tasks. We show the details of the dataset annotation pipeline and showcase four possible variants of datasets that can be obtained from our framework with example scenes, such as indoor reconstruction and SLAM, scene editing & object removal, human reconstruction and 6d pose estimation. Recent pipelines for indoor reconstruction and SLAM serve as new benchmarks. In contrast to previous indoor dataset, our design allows to evaluate dense geometry tasks on eleven sample scenes against accurately rendered ground truth depth maps.