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Found 11 papers, 9 papers with code
Dynamic 3D Point Cloud Sequences as 2D Videos
The structured nature of our SPCV representation allows for the seamless adaptation of well-established 2D image/video techniques, enabling efficient and effective processing and analysis of 3D point cloud sequences.
Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis
In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels.
CorrI2P: Deep Image-to-Point Cloud Registration via Dense Correspondence
Motivated by the intuition that the critical step of localizing a 2D image in the corresponding 3D point cloud is establishing 2D-3D correspondence between them, we propose the first feature-based dense correspondence framework for addressing the image-to-point cloud registration problem, dubbed CorrI2P, which consists of three modules, i. e., feature embedding, symmetric overlapping region detection, and pose estimation through the established correspondence.
Ranked #1 on
Image to Point Cloud Registration
on KITTI
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
We propose WarpingGAN, an effective and efficient 3D point cloud generation network.
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
One-Bit Compressive Sensing: Can We Go Deep and Blind?
The proposed deep architecture is able to learn an alternative sensing matrix by taking advantage of the underlying unfolded algorithm such that the resulting learned recovery algorithm can accurately and quickly (in terms of the number of iterations) recover the underlying compressed signal of interest from its one-bit noisy measurements.
DU-DARTS: Decreasing the Uncertainty of Differentiable Architecture Search
Differentiable Neural Architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency.
Ranked #9 on
Neural Architecture Search
on CIFAR-100
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds
The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence.
Ranked #6 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applications applied to the downsampled sparse points as much as possible.
See and Think: Disentangling Semantic Scene Completion
Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings.
VarNet: Exploring Variations for Unsupervised Video Prediction
For the KTH dataset, the VarNet outperforms the state-of-the-art works up to 11. 9% on PSNR and 9. 5% on SSIM.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
