# Uncertainty-aware 3D Object-Level Mapping with Deep Shape Priors arXiv: https://arxiv.org/abs/2309.09118 ยท HTML: https://mlsanity.com/paper/2309.09118/ ## Key facts - Cited by 8 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 17 Sep 2023. ## Builds on (most cited first, 30 of 30) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017) - [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015) - [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019) - [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021) - [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018) - [PointPillars: Fast Encoders for Object Detection from Point Clouds](https://mlsanity.com/paper/1812.05784/) (2018) - [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015) - [PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1812.11788/) (2018) - [DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion](https://mlsanity.com/paper/1901.04780/) (2019) - [Multi-view Supervision for Single-view Reconstruction via Differentiable Ray Consistency](https://mlsanity.com/paper/1704.06254/) (2017) - [Mask2Former for Video Instance Segmentation](https://mlsanity.com/paper/2112.10764/) (2021) - [Scan2CAD: Learning CAD Model Alignment in RGB-D Scans](https://mlsanity.com/paper/1811.11187/) (2018) - [Self-supervised 6D Object Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/1909.10159/) (2019) - [Fusion++: Volumetric Object-Level SLAM](https://mlsanity.com/paper/1808.08378/) (2018) - [MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects](https://mlsanity.com/paper/1804.09194/) (2018) - [PoseRBPF: A Rao-Blackwellized Particle Filter for 6D Object Pose Tracking](https://mlsanity.com/paper/1905.09304/) (2019) - [NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstruction](https://mlsanity.com/paper/2004.04485/) (2020) - [FroDO: From Detections to 3D Objects](https://mlsanity.com/paper/2005.05125/) (2020) - [Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors](https://mlsanity.com/paper/2101.05036/) (2021) - [Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos](https://mlsanity.com/paper/2012.04641/) (2020) - [DSP-SLAM: Object Oriented SLAM with Deep Shape Priors](https://mlsanity.com/paper/2108.09481/) (2021) - [Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation](https://mlsanity.com/paper/2205.01823/) (2022) - [SO-SLAM: Semantic Object SLAM with Scale Proportional and Symmetrical Texture Constraints](https://mlsanity.com/paper/2109.04884/) (2021) - [Learning Orientation Distributions for Object Pose Estimation](https://mlsanity.com/paper/2007.01418/) (2020) - [Robust Grasp Planning Over Uncertain Shape Completions](https://mlsanity.com/paper/1903.00645/) (2019) - [A Multi-Hypothesis Approach to Pose Ambiguity in Object-Based SLAM](https://mlsanity.com/paper/2108.01225/) (2021) - [Multi-view 3D Object Reconstruction and Uncertainty Modelling with Neural Shape Prior](https://mlsanity.com/paper/2306.11739/) (2023) - [6D Pose Estimation for Textureless Objects on RGB Frames using Multi-View Optimization](https://mlsanity.com/paper/2210.11554/) (2022) - [Active Pose Refinement for Textureless Shiny Objects using the Structured Light Camera](https://mlsanity.com/paper/2308.14665/) (2023) ## Built on by (most cited first, 8 of 8) - [Toward General Object-level Mapping from Sparse Views with 3D Diffusion Priors](https://mlsanity.com/paper/2410.05514/) (2024) - [Active 6D Pose Estimation for Textureless Objects using Multi-View RGB Frames](https://mlsanity.com/paper/2503.03726/) (2025) - [Uncertainty modeling for fine-tuned implicit functions](https://mlsanity.com/paper/2406.12082/) (2024) - [UnPose: Uncertainty-Guided Diffusion Priors for Zero-Shot Pose Estimation](https://mlsanity.com/paper/2508.15972/) (2025) - [Robust Bayesian Scene Reconstruction with Retrieval-Augmented Priors for Precise Grasping and Planning](https://mlsanity.com/paper/2411.19461/) (2024) - [Category-level Meta-learned NeRF Priors for Efficient Object Mapping](https://mlsanity.com/paper/2503.01582/) (2025) - [NF-SLAM: Effective, Normalizing Flow-supported Neural Field representations for object-level visual SLAM in automotive applications](https://mlsanity.com/paper/2503.11199/) (2025) - [MU-GeNeRF: Multi-view Uncertainty-guided Generalizable Neural Radiance Fields for Distractor-aware Scene](https://mlsanity.com/paper/2604.17965/) (2026) ## Abstract (arXiv) 3D object-level mapping is a fundamental problem in robotics, which is especially challenging when object CAD models are unavailable during inference. In this work, we propose a framework that can reconstruct high-quality object-level maps for unknown objects. Our approach takes multiple RGB-D images as input and outputs dense 3D shapes and 9-DoF poses (including 3 scale parameters) for detected objects. The core idea of our approach is to leverage a learnt generative model for shape categories as a prior and to formulate a probabilistic, uncertainty-aware optimization framework for 3D reconstruction. We derive a probabilistic formulation that propagates shape and pose uncertainty through two novel loss functions. Unlike current state-of-the-art approaches, we explicitly model the uncertainty of the object shapes and poses during our optimization, resulting in a high-quality object-level mapping system. Moreover, the resulting shape and pose uncertainties, which we demonstrate can accurately reflect the true errors of our object maps, can also be useful for downstream robotics tasks such as active vision. We perform extensive evaluations on indoor and outdoor real-world datasets, achieving achieves substantial improvements over state-of-the-art methods. Our code will be available at https://github.com/TRAILab/UncertainShapePose.