mlsanityData to 7 Oct 2026

ELLIPSDF: Joint Object Pose and Shape Optimization with a Bi-level Ellipsoid and Signed Distance Function Description

arXiv 2108.00355 ↗First posted 1 Aug 2021cs.CVcited by 10

Not on the map. Why?

Abstract (arXiv)

Autonomous systems need to understand the semantics and geometry of their surroundings in order to comprehend and safely execute object-level task specifications. This paper proposes an expressive yet compact model for joint object pose and shape optimization, and an associated optimization algorithm to infer an object-level map from multi-view RGB-D camera observations. The model is expressive because it captures the identities, positions, orientations, and shapes of objects in the environment. It is compact because it relies on a low-dimensional latent representation of implicit object shape, allowing onboard storage of large multi-category object maps. Different from other works that rely on a single object representation format, our approach has a bi-level object model that captures both the coarse level scale as well as the fine level shape details. Our approach is evaluated on the large-scale real-world ScanNet dataset and compared against state-of-the-art methods.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 30 of its 51 references that are in the map. First posted on arXiv 1 Aug 2021.

Builds on

30 of 51 references.

References · 30

  1. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,037
  5. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  6. 2016Simple Online and Realtime Trackingcited by 862
  7. 2018PartNet: A Large-scale Benchmark for Fine-grained and Hierarchical Part-level 3D Object Understandingcited by 521
  8. 2019Cascade R-CNN: High Quality Object Detection and Instance Segmentationcited by 428
  9. 2019Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mappingcited by 181
  10. 2019ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residualscited by 180
  11. 2018Scan2CAD: Learning CAD Model Alignment in RGB-D Scanscited by 142
  12. 2018CubeSLAM: Monocular 3D Object SLAMcited by 101
18 more
  1. 2018Fusion++: Volumetric Object-Level SLAMcited by 97
  2. 2018QuadricSLAM: Dual Quadrics from Object Detections as Landmarks in Object-oriented SLAMcited by 86
  3. 2020DualSDF: Semantic Shape Manipulation using a Two-Level Representationcited by 82
  4. 2020NodeSLAM: Neural Object Descriptors for Multi-View Shape Reconstructioncited by 61
  5. 2020FroDO: From Detections to 3D Objectscited by 51
  6. 2019End-to-End CAD Model Retrieval and 9DoF Alignment in 3D Scanscited by 40
  7. 2020Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videoscited by 33
  8. 2016Meaningful Maps With Object-Oriented Semantic Mappingcited by 31
  9. 2020Learning to Group: A Bottom-Up Framework for 3D Part Discovery in Unseen Categoriescited by 29
  10. 2018Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoningcited by 27
  11. 2020EAO-SLAM: Monocular Semi-Dense Object SLAM Based on Ensemble Data Associationcited by 24
  12. 2019Real-time Scalable Dense Surfel Mappingcited by 19
  13. 2020MOLTR: Multiple Object Localisation, Tracking, and Reconstruction from Monocular RGB Videoscited by 18
  14. 2020CAD-Deform: Deformable Fitting of CAD Models to 3D Scanscited by 17
  15. 2016Beyond Holistic Object Recognition: Enriching Image Understanding with Part Statescited by 13
  16. 2019Deep-SLAM++: Object-level RGBD SLAM based on class-specific deep shape priorscited by 8
  17. 2017SLAM with Objects using a Nonparametric Pose Graphcited by 7
  18. 2020Extending DeepSDF for automatic 3D shape retrieval and similarity transform estimationcited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 1; 2026: 1.

10 papers.

  1. 20246DGS: 6D Pose Estimation from a Single Image and a 3D Gaussian Splatting Modelcited by 28
  2. 2022Generative Category-Level Shape and Pose Estimation with Semantic Primitivescited by 19
  3. 2022RayTran: 3D pose estimation and shape reconstruction of multiple objects from videos with ray-traced transformerscited by 17
  4. 2023Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environmentscited by 15
  5. 2023O2-Recon: Completing 3D Reconstruction of Occluded Objects in the Scene with a Pre-trained 2D Diffusion Modelcited by 8
  6. 2023TransPoser: Transformer as an Optimizer for Joint Object Shape and Pose Estimationcited by 2
  7. 2025Box Pose and Shape Estimation and Domain Adaptation for Large-Scale Warehouse Automationcited by 2
  8. 2024ShapeICP: Iterative Category-level Object Pose and Shape Estimation from Depthcited by 1
  9. 2022Taylor3DNet: Fast 3D Shape Inference With Landmark Points Based Taylor Seriescited by 0
  10. 2026Hydra++: Real-Time Hierarchical 3D Scene Graph Construction With Object-Level Shape Estimationcited by 0