3D Shape Modeling

10 papers with code • 2 benchmarks • 1 datasets


Most implemented papers

Mesh R-CNN

facebookresearch/pytorch3d ICCV 2019

We propose a system that detects objects in real-world images and produces a triangle mesh giving the full 3D shape of each detected object.

Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations

koryako/selfcarRaspberryPi 24 Aug 2017

Our contributions are fourfold: (1) To best of our knowledge, we are presenting for the first time a method to learn a 6-DOF grasping net from RGBD input; (2) We build a grasping dataset from demonstrations in virtual reality with rich sensory and interaction annotations.

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

xingyuansun/pix3d CVPR 2018

We study 3D shape modeling from a single image and make contributions to it in three aspects.

Learning to Dress 3D People in Generative Clothing

QianliM/CAPE CVPR 2020

To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.

GSNet: Joint Vehicle Pose and Shape Reconstruction with Geometrical and Scene-aware Supervision

lkeab/gsnet ECCV 2020

GSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.

Object Shape Error Response Using Bayesian 3-D Convolutional Neural Networks for Assembly Systems With Compliant Parts

sumitsinha/Deep_Learning_for_Manufacturing 8 Dec 2021

The paper proposes a novel Object Shape Error Response (OSER) approach to estimate the dimensional and geometric variation of assembled products and then, relate, these to process parameters, which can be interpreted as root causes (RC) of the object shape defects.

SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation

amirhertz/spaghetti 31 Jan 2022

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.

3DILG: Irregular Latent Grids for 3D Generative Modeling

1zb/3DILG 27 May 2022

All probabilistic experiments confirm that we are able to generate detailed and high quality shapes to yield the new state of the art in generative 3D shape modeling.

LoRD: Local 4D Implicit Representation for High-Fidelity Dynamic Human Modeling

BoyanJIANG/LoRD 18 Aug 2022

Recent progress in 4D implicit representation focuses on globally controlling the shape and motion with low dimensional latent vectors, which is prone to missing surface details and accumulating tracking error.

3D VR Sketch Guided 3D Shape Prototyping and Exploration

rowl1ng/3dsketch2shape ICCV 2023

3D shape modeling is labor-intensive, time-consuming, and requires years of expertise.