mlsanityData to 7 Oct 2026

TSDF++: A Multi-Object Formulation for Dynamic Object Tracking and Reconstruction

arXiv 2105.07468 ↗First posted 16 May 2021cs.CV cs.ROcited by 4

Not on the map. Why?

Abstract (arXiv)

The ability to simultaneously track and reconstruct multiple objects moving in the scene is of the utmost importance for robotic tasks such as autonomous navigation and interaction. Virtually all of the previous attempts to map multiple dynamic objects have evolved to store individual objects in separate reconstruction volumes and track the relative pose between them. While simple and intuitive, such formulation does not scale well with respect to the number of objects in the scene and introduces the need for an explicit occlusion handling strategy. In contrast, we propose a map representation that allows maintaining a single volume for the entire scene and all the objects therein. To this end, we introduce a novel multi-object TSDF formulation that can encode multiple object surfaces at any given location in the map. In a multiple dynamic object tracking and reconstruction scenario, our representation allows maintaining accurate reconstruction of surfaces even while they become temporarily occluded by other objects moving in their proximity. We evaluate the proposed TSDF++ formulation on a public synthetic dataset and demonstrate its ability to preserve reconstructions of occluded surfaces when compared to the standard TSDF map representation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 25 references that are in the map. First posted on arXiv 16 May 2021.

Builds on

17 of 25 references.

References · 17

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2017Mask R-CNNcited by 7,259
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  4. 2016Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planningcited by 148
  5. 2016SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networkscited by 134
  6. 2018Fusion++: Volumetric Object-Level SLAMcited by 97
  7. 2018MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objectscited by 95
  8. 2019Volumetric Instance-Aware Semantic Mapping and 3D Object Discoverycited by 79
  9. 2016VolumeDeform: Real-time Volumetric Non-rigid Reconstructioncited by 75
  10. 2018MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAMcited by 65
  11. 2017Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objectscited by 47
  12. 2016Meaningful Maps With Object-Oriented Semantic Mappingcited by 31
5 more
  1. 2019EM-Fusion: Dynamic Object-Level SLAM with Probabilistic Data Associationcited by 28
  2. 2019Robust Dense Mapping for Large-Scale Dynamic Environmentscited by 21
  3. 2018Real-time Progressive 3D Semantic Segmentation for Indoor Scenecited by 18
  4. 2018Incremental Object Database: Building 3D Models from Multiple Partial Observationscited by 15
  5. 2019Live Reconstruction of Large-Scale Dynamic Outdoor Worldscited by 2

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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 1.

4 papers.