# NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction

arXiv: https://arxiv.org/abs/2004.04485 · HTML: https://mlsanity.com/paper/2004.04485/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 30 of 61)

- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting](https://mlsanity.com/paper/2311.11700/) (2023)
- [NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM](https://mlsanity.com/paper/2302.03594/) (2023)
- [ESLAM: Efficient Dense SLAM System Based on Hybrid Representation of Signed Distance Fields](https://mlsanity.com/paper/2211.11704/) (2022)
- [SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM](https://mlsanity.com/paper/2402.03246/) (2024)
- [vMAP: Vectorised Object Mapping for Neural Field SLAM](https://mlsanity.com/paper/2302.01838/) (2023)
- [Theseus: A Library for Differentiable Nonlinear Optimization](https://mlsanity.com/paper/2207.09442/) (2022)
- [Dense RGB SLAM with Neural Implicit Maps](https://mlsanity.com/paper/2301.08930/) (2023)
- [SAIL-VOS 3D: A Synthetic Dataset and Baselines for Object Detection and 3D Mesh Reconstruction from Video Data](https://mlsanity.com/paper/2105.08612/) (2021)
- [DSP-SLAM: Object Oriented SLAM with Deep Shape Priors](https://mlsanity.com/paper/2108.09481/) (2021)
- [ILabel: Interactive Neural Scene Labelling](https://mlsanity.com/paper/2111.14637/) (2021)
- [DNS SLAM: Dense Neural Semantic-Informed SLAM](https://mlsanity.com/paper/2312.00204/) (2023)
- [Generative Category-Level Shape and Pose Estimation with Semantic Primitives](https://mlsanity.com/paper/2210.01112/) (2022)
- [MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction](https://mlsanity.com/paper/2402.03762/) (2024)
- [Advances in Inference and Representation for Simultaneous Localization and Mapping](https://mlsanity.com/paper/2103.05041/) (2021)
- [ObjectMatch: Robust Registration using Canonical Object Correspondences](https://mlsanity.com/paper/2212.01985/) (2022)
- [An Object SLAM Framework for Association, Mapping, and High-Level Tasks](https://mlsanity.com/paper/2305.07299/) (2023)
- [SuperPrimitive: Scene Reconstruction at a Primitive Level](https://mlsanity.com/paper/2312.05889/) (2023)
- [SEGS-SLAM: Structure-enhanced 3D Gaussian Splatting SLAM with Appearance Embedding](https://mlsanity.com/paper/2501.05242/) (2025)
- [ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description](https://mlsanity.com/paper/2108.00355/) (2021)
- [NeuSE: Neural SE(3)-Equivariant Embedding for Long-Term Object-Based Simultaneous Localization and Mapping](https://mlsanity.com/paper/2303.07308/) (2023)
- [Get a Grip: Reconstructing Hand-Object Stable Grasps in Egocentric Videos](https://mlsanity.com/paper/2312.15719/) (2023)
- [Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior](https://mlsanity.com/paper/2306.11739/) (2023)
- [HRBF-Fusion: Accurate 3D reconstruction from RGB-D data using on-the-fly implicits](https://mlsanity.com/paper/2202.01829/) (2022)
- [Break and Make: Interactive Structural Understanding Using LEGO Bricks](https://mlsanity.com/paper/2207.13738/) (2022)
- [Learning to Complete Object Shapes for Object-level Mapping in Dynamic Scenes](https://mlsanity.com/paper/2208.05067/) (2022)
- [You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example](https://mlsanity.com/paper/2305.12626/) (2023)
- [Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors](https://mlsanity.com/paper/2309.09118/) (2023)

## Abstract (arXiv)

The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables. We present efficient and optimisable multi-class learned object descriptors together with a novel probabilistic and differential rendering engine, for principled full object shape inference from one or more RGB-D images. Our framework allows for accurate and robust 3D object reconstruction which enables multiple applications including robot grasping and placing, augmented reality, and the first object-level SLAM system capable of optimising object poses and shapes jointly with camera trajectory.