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Found 16 papers, 13 papers with code
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance
We introduce a new deep learning method for point cloud comparison.
PatchContrast: Self-Supervised Pre-training for 3D Object Detection
Accurately detecting objects in the environment is a key challenge for autonomous vehicles.
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System
It is composed of a visual servoing module that reaches and grasps assembly parts in an unstructured multi-instance and dynamic environment, an action recognition module that performs human action prediction for implicit communication, and a visual handover module that uses the perceptual understanding of human behaviour to produce an intuitive and efficient collaborative assembly experience.
Aligning Step-by-Step Instructional Diagrams to Video Demonstrations
In this paper, we consider a novel setting where such an alignment is between (i) instruction steps that are depicted as assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video segments from in-the-wild videos; these videos comprising an enactment of the assembly actions in the real world.
3DInAction: Understanding Human Actions in 3D Point Clouds
We propose a novel method for 3D point cloud action recognition.
Octree Guided Unoriented Surface Reconstruction
We propose a two-step approach, OG-INR, where we (1) construct a discrete octree and label what is inside and outside (2) optimize for a continuous and high-fidelity shape using an INR that is initially guided by the octree's labelling.
GraVoS: Voxel Selection for 3D Point-Cloud Detection
3D object detection within large 3D scenes is challenging not only due to the sparsity and irregularity of 3D point clouds, but also due to both the extreme foreground-background scene imbalance and class imbalance.
DiGS: Divergence Guided Shape Implicit Neural Representation for Unoriented Point Clouds
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
CloudWalker: Random walks for 3D point cloud shape analysis
This work presents a different approach for representing and learning the shape from a given point set.
DiGS : Divergence guided shape implicit neural representation for unoriented point clouds
In this paper, we propose a divergence guided shape representation learning approach that does not require normal vectors as input.
The IKEA ASM Dataset: Understanding People Assembling Furniture through Actions, Objects and Pose
The availability of a large labeled dataset is a key requirement for applying deep learning methods to solve various computer vision tasks.
DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
We introduce a new deep learning method for point cloud comparison.
DeepFit: 3D Surface Fitting via Neural Network Weighted Least Squares
We propose a surface fitting method for unstructured 3D point clouds.
Ranked #6 on
Surface Normals Estimation
on PCPNet
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
In this paper, we propose a normal estimation method for unstructured 3D point clouds.
Ranked #8 on
Surface Normals Estimation
on PCPNet
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
Ranked #57 on
3D Part Segmentation
on ShapeNet-Part
Graph Based Over-Segmentation Methods for 3D Point Clouds
This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points.
