# SSP-Pose: Symmetry-Aware Shape Prior Deformation for Direct Category-Level Object Pose Estimation

arXiv: https://arxiv.org/abs/2208.06661 · HTML: https://mlsanity.com/paper/2208.06661/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 45 references that are in the map.
- First posted on arXiv 13 Aug 2022.

## Builds on (most cited first, 30 of 37)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes](https://mlsanity.com/paper/1711.00199/) (2017)
- [On the Variance of the Adaptive Learning Rate and Beyond](https://mlsanity.com/paper/1908.03265/) (2019)
- [Multi-View 3D Object Detection Network for Autonomous Driving](https://mlsanity.com/paper/1611.07759/) (2016)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [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)
- [Lookahead Optimizer: k steps forward, 1 step back](https://mlsanity.com/paper/1907.08610/) (2019)
- [CosyPose: Consistent multi-view multi-object 6D pose estimation](https://mlsanity.com/paper/2008.08465/) (2020)
- [DeepIM: Deep Iterative Matching for 6D Pose Estimation](https://mlsanity.com/paper/1804.00175/) (2018)
- [SSD-6D: Making RGB-based 3D detection and 6D pose estimation great again](https://mlsanity.com/paper/1711.10006/) (2017)
- [Pix2Pose: Pixel-Wise Coordinate Regression of Objects for 6D Pose Estimation](https://mlsanity.com/paper/1908.07433/) (2019)
- [Real-Time Seamless Single Shot 6D Object Pose Prediction](https://mlsanity.com/paper/1711.08848/) (2017)
- [PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation](https://mlsanity.com/paper/1911.04231/) (2019)
- [Shape Prior Deformation for Categorical 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2007.08454/) (2020)
- [Geometry Uncertainty Projection Network for Monocular 3D Object Detection](https://mlsanity.com/paper/2107.13774/) (2021)
- [EPOS: Estimating 6D Pose of Objects with Symmetries](https://mlsanity.com/paper/2004.00605/) (2020)
- [FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation](https://mlsanity.com/paper/2103.02242/) (2021)
- [Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/2001.09322/) (2020)
- [FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism](https://mlsanity.com/paper/2103.07054/) (2021)
- [HybridPose: 6D Object Pose Estimation under Hybrid Representations](https://mlsanity.com/paper/2001.01869/) (2020)
- [DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency](https://mlsanity.com/paper/2103.06526/) (2021)
- [Self-supervised 6D Object Pose Estimation for Robot Manipulation](https://mlsanity.com/paper/1909.10159/) (2019)
- [GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting](https://mlsanity.com/paper/2203.07918/) (2022)
- [Single-Stage 6D Object Pose Estimation](https://mlsanity.com/paper/1911.08324/) (2019)
- [SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation](https://mlsanity.com/paper/2108.08367/) (2021)
- [BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models](https://mlsanity.com/paper/2108.00516/) (2021)
- [CAPTRA: CAtegory-level Pose Tracking for Rigid and Articulated Objects from Point Clouds](https://mlsanity.com/paper/2104.03437/) (2021)

## Built on by (most cited first, 30 of 41)

- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [SecondPose: SE(3)-Consistent Dual-Stream Feature Fusion for Category-Level Pose Estimation](https://mlsanity.com/paper/2311.11125/) (2023)
- [HS-Pose: Hybrid Scope Feature Extraction for Category-level Object Pose Estimation](https://mlsanity.com/paper/2303.15743/) (2023)
- [GenPose: Generative Category-level Object Pose Estimation via Diffusion Models](https://mlsanity.com/paper/2306.10531/) (2023)
- [HANDAL: A Dataset of Real-World Manipulable Object Categories with Pose Annotations, Affordances, and Reconstructions](https://mlsanity.com/paper/2308.01477/) (2023)
- [VI-Net: Boosting Category-level 6D Object Pose Estimation via Learning Decoupled Rotations on the Spherical Representations](https://mlsanity.com/paper/2308.09916/) (2023)
- [Deep Learning-Based Object Pose Estimation: A Comprehensive Survey](https://mlsanity.com/paper/2405.07801/) (2024)
- [SG-Bot: Object Rearrangement via Coarse-to-Fine Robotic Imagination on Scene Graphs](https://mlsanity.com/paper/2309.12188/) (2023)
- [DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field](https://mlsanity.com/paper/2308.08231/) (2023)
- [SceneMaker: Open-set 3D Scene Generation with Decoupled De-occlusion and Pose Estimation Model](https://mlsanity.com/paper/2512.10957/) (2025)
- [CLIPose: Category-Level Object Pose Estimation with Pre-trained Vision-Language Knowledge](https://mlsanity.com/paper/2402.15726/) (2024)
- [LaPose: Laplacian Mixture Shape Modeling for RGB-Based Category-Level Object Pose Estimation](https://mlsanity.com/paper/2409.15727/) (2024)
- [U-RED: Unsupervised 3D Shape Retrieval and Deformation for Partial Point Clouds](https://mlsanity.com/paper/2308.06383/) (2023)
- [MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision](https://mlsanity.com/paper/2310.11696/) (2023)
- [UNOPose: Unseen Object Pose Estimation with an Unposed RGB-D Reference Image](https://mlsanity.com/paper/2411.16106/) (2024)
- [UA-Pose: Uncertainty-Aware 6D Object Pose Estimation and Online Object Completion with Partial References](https://mlsanity.com/paper/2506.07996/) (2025)
- [ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation](https://mlsanity.com/paper/2311.11106/) (2023)
- [GeoReF: Geometric Alignment Across Shape Variation for Category-level Object Pose Refinement](https://mlsanity.com/paper/2404.11139/) (2024)
- [KP-RED: Exploiting Semantic Keypoints for Joint 3D Shape Retrieval and Deformation](https://mlsanity.com/paper/2403.10099/) (2024)
- [Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models](https://mlsanity.com/paper/2408.07975/) (2024)
- [GIVEPose: Gradual Intra-class Variation Elimination for RGB-based Category-Level Object Pose Estimation](https://mlsanity.com/paper/2503.15110/) (2025)
- [DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field](https://mlsanity.com/paper/2308.02239/) (2023)
- [MonoDiff9D: Monocular Category-Level 9D Object Pose Estimation via Diffusion Model](https://mlsanity.com/paper/2504.10433/) (2025)
- [Pose Prior Learner: Unsupervised Categorical Prior Learning for Pose Estimation](https://mlsanity.com/paper/2410.03858/) (2024)
- [Street Gaussians without 3D Object Tracker](https://mlsanity.com/paper/2412.05548/) (2024)
- [RGB-D-Based Categorical Object Pose and Shape Estimation: Methods, Datasets, and Evaluation](https://mlsanity.com/paper/2301.08147/) (2023)
- [ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depth](https://mlsanity.com/paper/2408.13147/) (2024)
- [Object Pose Estimation Using Implicit Representation For Transparent Objects](https://mlsanity.com/paper/2410.13465/) (2024)
- [Category-Level 6D Object Pose Estimation in Agricultural Settings Using a Lattice-Deformation Framework and Diffusion-Augmented Synthetic Data](https://mlsanity.com/paper/2505.24636/) (2025)
- [Joint Learning of Pose Regression and Denoising Diffusion with Score Scaling Sampling for Category-level 6D Pose Estimation](https://mlsanity.com/paper/2510.04125/) (2025)

## Abstract (arXiv)

Category-level pose estimation is a challenging problem due to intra-class shape variations. Recent methods deform pre-computed shape priors to map the observed point cloud into the normalized object coordinate space and then retrieve the pose via post-processing, i.e., Umeyama's Algorithm. The shortcomings of this two-stage strategy lie in two aspects: 1) The surrogate supervision on the intermediate results can not directly guide the learning of pose, resulting in large pose error after post-processing. 2) The inference speed is limited by the post-processing step. In this paper, to handle these shortcomings, we propose an end-to-end trainable network SSP-Pose for category-level pose estimation, which integrates shape priors into a direct pose regression network. SSP-Pose stacks four individual branches on a shared feature extractor, where two branches are designed to deform and match the prior model with the observed instance, and the other two branches are applied for directly regressing the totally 9 degrees-of-freedom pose and performing symmetry reconstruction and point-wise inlier mask prediction respectively. Consistency loss terms are then naturally exploited to align the outputs of different branches and promote the performance. During inference, only the direct pose regression branch is needed. In this manner, SSP-Pose not only learns category-level pose-sensitive characteristics to boost performance but also keeps a real-time inference speed. Moreover, we utilize the symmetry information of each category to guide the shape prior deformation, and propose a novel symmetry-aware loss to mitigate the matching ambiguity. Extensive experiments on public datasets demonstrate that SSP-Pose produces superior performance compared with competitors with a real-time inference speed at about 25Hz.