3D Shape Modeling
11 papers with code • 2 benchmarks • 3 datasets
Image: Gkioxari et al
Benchmarks
These leaderboards are used to track progress in 3D Shape Modeling
| Trend | Dataset | Best Model | Paper | Code | Compare |
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Pix3D
SynFoot
DrivAerNet
Most implemented papers
Mesh R-CNN
facebookresearch/pytorch3d
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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.
Temporal 3D Shape Modeling for Video-Based Cloth-Changing Person Re-Identification
dustin-nguyen-qil/SEMI_VCCReID
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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops 2024
In this work, we propose "Temporal 3D ShapE Modeling for VCCRe-ID" (SEMI), a lightweight end-to-end framework that addresses these issues by learning human 3D shape representations.
Learning 6-DOF Grasping Interaction via Deep Geometry-aware 3D Representations
koryako/selfcarRaspberryPi
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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
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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
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
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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
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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
Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.
3DILG: Irregular Latent Grids for 3D Generative Modeling
1zb/3DILG
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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
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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.
