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Found 11 papers, 2 papers with code
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention
Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices.
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
DELO: Deep Evidential LiDAR Odometry using Partial Optimal Transport
In this work, we propose (i) partial optimal transportation of LiDAR feature descriptor for robust LO estimation, (ii) joint learning of predictive uncertainty while learning odometry over driving sequences, and (iii) demonstrate how PU can serve as evidence for necessary pose-graph optimization when LO network is either under or over confident.
CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
TSCom-Net: Coarse-to-Fine 3D Textured Shape Completion Network
We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans.
HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks
To address the limitations of the existing methods, we develop HandVoxNet++, i. e., a voxel-based deep network with 3D and graph convolutions trained in a fully supervised manner.
RPSRNet: End-to-End Trainable Rigid Point Set Registration Network Using Barnes-Hut 2D-Tree Representation
For this task, we use a novel 2^D-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network.
RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut $2^D$-Tree Representation
For this task, we use a novel $2^D$-tree representation for the input point sets and a hierarchical deep feature embedding in the neural network.
Fast Gravitational Approach for Rigid Point Set Registration with Ordinary Differential Equations
This article introduces a new physics-based method for rigid point set alignment called Fast Gravitational Approach (FGA).
HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map
The input to our method is a 3D voxelized depth map, and we rely on two hand shape representations.
Gravitational Approach for Point Set Registration
In this paper a new astrodynamics inspired rigid point set registration algorithm is introduced -- the Gravitational Approach (GA).
