3D Point Cloud Reconstruction

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

3 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

13 shown of 13 papers with code (22 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
  7 Aug 2018 3 repositories listed
  LiDAR sensors are employed to provide the 3D point cloud reconstruction of the surrounding environment, while the task of 3D object bounding box detection in real time remains a strong algorithmic challenge.
• SampleNet: Differentiable Point Cloud Sampling
  8 Dec 2019 2 repositories listed
  As the size of the point cloud grows, so do the computational demands of these tasks.
• Local and Global Point Cloud Reconstruction for 3D Hand Pose Estimation
  13 Dec 2021 1 repository listed
  This paper addresses the 3D point cloud reconstruction and 3D pose estimation of the human hand from a single RGB image.
• Canonical Capsules: Self-Supervised Capsules in Canonical Pose
  8 Dec 2020 1 repository listed
  We propose a self-supervised capsule architecture for 3D point clouds.
• From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
  5 May 2020 1 repository listed
  We learn both 3D point cloud reconstruction and pose estimation networks in a self-supervised manner, making use of differentiable point cloud renderer to train with 2D supervision.
• Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
  22 Apr 2020 1 repository listed Syntology ran 3 of 6 samples · 3 unverified
  Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem.
• Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
  6 Dec 2019 1 repository listed
  n this paper, we propose an effective and efficient pyramid multi-view stereo (MVS) net with self-adaptive view aggregation for accurate and complete dense point cloud reconstruction.
• Real-time Scalable Dense Surfel Mapping
  10 Sep 2019 1 repository listed
  First, superpixels extracted from both intensity and depth images are used to model surfels in the system.
• Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
  25 Jan 2019 1 repository listed
  Through extensive quantitative and qualitative evaluation on synthetic and real datasets, we demonstrate that DensePCR outperforms the existing state-of-the-art point cloud reconstruction works, while also providing a…
• Learning to Sample
  4 Dec 2018 1 repository listed
  We show that it is better to learn how to sample.
• CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
  28 Nov 2018 1 repository listed
  We consider the task of single image 3D point cloud reconstruction, and aim to utilize multiple foreground masks as our supervisory data to alleviate the need for large scale 3D datasets.
• 3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
  30 Sep 2018 1 repository listed Syntology ran 0 of 3 samples · 3 unverified
  We propose a mechanism to reconstruct part annotated 3D point clouds of objects given just a single input image.
• 3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
  20 Jul 2018 1 repository listed Syntology ran 1 of 7 samples · 6 unverified
  3D reconstruction from single view images is an ill-posed problem.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.