mlsanityData to 7 Oct 2026

MonteBoxFinder: Detecting and Filtering Primitives to Fit a Noisy Point Cloud

arXiv 2207.14268 ↗First posted 28 Jul 2022cs.CVcited by 11

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Abstract (arXiv)

We present MonteBoxFinder, a method that, given a noisy input point cloud, fits cuboids to the input scene. Our primary contribution is a discrete optimization algorithm that, from a dense set of initially detected cuboids, is able to efficiently filter good boxes from the noisy ones. Inspired by recent applications of MCTS to scene understanding problems, we develop a stochastic algorithm that is, by design, more efficient for our task. Indeed, the quality of a fit for a cuboid arrangement is invariant to the order in which the cuboids are added into the scene. We develop several search baselines for our problem and demonstrate, on the ScanNet dataset, that our approach is more efficient and precise. Finally, we strongly believe that our core algorithm is very general and that it could be extended to many other problems in 3D scene understanding.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 15 of its 32 references that are in the map. First posted on arXiv 28 Jul 2022.

Builds on

15 of 32 references.

References · 15

  1. 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2019PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detectioncited by 890
  4. 2019Deep Hough Voting for 3D Object Detection in Point Cloudscited by 648
  5. 2019Deep Learning for 3D Point Clouds: A Surveycited by 557
  6. 2020Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloudcited by 335
  7. 2016Learning Shape Abstractions by Assembling Volumetric Primitivescited by 181
  8. 2019Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboidscited by 123
  9. 2019Learning elementary structures for 3D shape generation and matchingcited by 98
  10. 2016IM2CADcited by 78
  11. 2020Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Imagecited by 72
  12. 2019Holistic++ Scene Understanding: Single-view 3D Holistic Scene Parsing and Human Pose Estimation with Human-Object Interaction and Physical Commonsensecited by 71
3 more
  1. 2018Holistic 3D Scene Parsing and Reconstruction from a Single RGB Imagecited by 69
  2. 2021Monte Carlo Scene Search for 3D Scene Understandingcited by 19
  3. 2021Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Imagescited by 17

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 2.

11 papers.

  1. 2023Differentiable Blocks World: Qualitative 3D Decomposition by Rendering Primitivescited by 37
  2. 2025SuperDec: 3D Scene Decomposition with Superquadric Primitivescited by 19
  3. 2024AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddingscited by 13
  4. 2024Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadricscited by 8
  5. 2023Convex Decomposition of Indoor Scenescited by 6
  6. 2023Learnable Earth Parser: Discovering 3D Prototypes in Aerial Scanscited by 5
  7. 2023Blocks2World: Controlling Realistic Scenes with Editable Primitivescited by 4
  8. 2024Improved Convex Decomposition with Ensembling and Negative Primitivescited by 0
  9. 2025Deep Polycuboid Fitting for Compact 3D Representation of Indoor Scenescited by 0
  10. 2026D-Prism: Differentiable Primitives for Structured Dynamic Modelingcited by 0
  11. 2026Superquadric Primitive Decomposition of 3D point clouds via Geometric-Aware Inlier Refinementcited by 0