3D Point Cloud Reconstruction
13 papers with code • 0 benchmarks • 3 datasets
Encoding and reconstruction of 3D point clouds.
Benchmarks
These leaderboards are used to track progress in 3D Point Cloud Reconstruction
HUMAN4D
IBISCape
Boombox
Most implemented papers
YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud
maudzung/YOLO3D-YOLOv4-PyTorch
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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
itailang/SampleNet
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CVPR 2020
As the size of the point cloud grows, so do the computational demands of these tasks.
3D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image
3D reconstruction from single view images is an ill-posed problem.
3D-PSRNet: Part Segmented 3D Point Cloud Reconstruction From a Single Image
We propose a mechanism to reconstruct part annotated 3D point clouds of objects given just a single input image.
CAPNet: Continuous Approximation Projection For 3D Point Cloud Reconstruction Using 2D Supervision
val-iisc/capnet
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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.
Learning to Sample
orendv/learning_to_sample
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CVPR 2019
We show that it is better to learn how to sample.
Dense 3D Point Cloud Reconstruction Using a Deep Pyramid Network
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 light-weight and scalable architecture for predicting high-resolution outputs.
Real-time Scalable Dense Surfel Mapping
First, superpixels extracted from both intensity and depth images are used to model surfels in the system.
Pyramid Multi-view Stereo Net with Self-adaptive View Aggregation
yhw-yhw/PVAMVSNet
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ECCV 2020
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.
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
lzqsd/TransparentShapeReconstruction
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CVPR 2020
Recovering the 3D shape of transparent objects using a small number of unconstrained natural images is an ill-posed problem.
