# RevealNet: Seeing Behind Objects in RGB-D Scans

arXiv: https://arxiv.org/abs/1904.12012 · HTML: https://mlsanity.com/paper/1904.12012/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2019.

## Built on by (most cited first, 30 of 30)

- [TransformerFusion: Monocular RGB Scene Reconstruction using Transformers](https://mlsanity.com/paper/2107.02191/) (2021)
- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [Panoptic 3D Scene Reconstruction From a Single RGB Image](https://mlsanity.com/paper/2111.02444/) (2021)
- [UniT3D: A Unified Transformer for 3D Dense Captioning and Visual Grounding](https://mlsanity.com/paper/2212.00836/) (2022)
- [Pri3D: Can 3D Priors Help 2D Representation Learning?](https://mlsanity.com/paper/2104.11225/) (2021)
- [Semantic Scene Completion via Integrating Instances and Scene in-the-Loop](https://mlsanity.com/paper/2104.03640/) (2021)
- [D3Net: A Unified Speaker-Listener Architecture for 3D Dense Captioning and Visual Grounding](https://mlsanity.com/paper/2112.01551/) (2021)
- [NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2307.14620/) (2023)
- [PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness](https://mlsanity.com/paper/2312.02158/) (2023)
- [UnScene3D: Unsupervised 3D Instance Segmentation for Indoor Scenes](https://mlsanity.com/paper/2303.14541/) (2023)
- [Learning 3D Scene Priors with 2D Supervision](https://mlsanity.com/paper/2211.14157/) (2022)
- [ShapeR: Robust Conditional 3D Shape Generation from Casual Captures](https://mlsanity.com/paper/2601.11514/) (2026)
- [Point Scene Understanding via Disentangled Instance Mesh Reconstruction](https://mlsanity.com/paper/2203.16832/) (2022)
- [BUOL: A Bottom-Up Framework with Occupancy-aware Lifting for Panoptic 3D Scene Reconstruction From A Single Image](https://mlsanity.com/paper/2306.00965/) (2023)
- [Mask3D: Pre-training 2D Vision Transformers by Learning Masked 3D Priors](https://mlsanity.com/paper/2302.14746/) (2023)
- [PCR-CG: Point Cloud Registration via Deep Explicit Color and Geometry](https://mlsanity.com/paper/2302.14418/) (2023)
- [Semantic Scene Completion with Cleaner Self](https://mlsanity.com/paper/2303.09977/) (2023)
- [A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes](https://mlsanity.com/paper/2403.07469/) (2024)
- [Zero-Shot Multi-Object Scene Completion](https://mlsanity.com/paper/2403.14628/) (2024)
- [SSR-2D: Semantic 3D Scene Reconstruction from 2D Images](https://mlsanity.com/paper/2302.03640/) (2023)
- [Neural Part Priors: Learning to Optimize Part-Based Object Completion in RGB-D Scans](https://mlsanity.com/paper/2203.09375/) (2022)
- [Multimodal Sense-Informed Prediction of 3D Human Motions](https://mlsanity.com/paper/2405.02911/) (2024)
- [LASA: Instance Reconstruction from Real Scans using A Large-scale Aligned Shape Annotation Dataset](https://mlsanity.com/paper/2312.12418/) (2023)
- [RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion](https://mlsanity.com/paper/2506.05285/) (2025)
- [Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database](https://mlsanity.com/paper/2207.01339/) (2022)
- [UniPlane: Unified Plane Detection and Reconstruction from Posed Monocular Videos](https://mlsanity.com/paper/2407.03594/) (2024)
- [Shape Anchor Guided Holistic Indoor Scene Understanding](https://mlsanity.com/paper/2309.11133/) (2023)
- [Point-based Instance Completion with Scene Constraints](https://mlsanity.com/paper/2504.05698/) (2025)
- [SAMURAI: Shape-Aware Multimodal Retrieval for 3D Object Identification](https://mlsanity.com/paper/2506.21056/) (2025)
- [GARDEN: Gravity-Aligned Reconstruction of Disentangled ENvironments from RGB images](https://mlsanity.com/paper/2606.03921/) (2026)

## Abstract (arXiv)

During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an object. Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry. This will open up new possibilities for interactions with objects in a scene, for instance for virtual or robotic agents. We tackle this problem by introducing RevealNet, a new data-driven approach that jointly detects object instances and predicts their complete geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts. RevealNet is an end-to-end 3D neural network architecture that leverages joint color and geometry feature learning. The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass. We show that predicting complete object geometry improves both 3D detection and instance segmentation performance. We evaluate on both real and synthetic scan benchmark data for the new task, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.