Diffusion-Based Signed Distance Fields for 3D Shape Generation-Reference-Cited by-同舟云学术

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Diffusion-Based Signed Distance Fields for 3D Shape Generation

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
language:
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Author:

Shim Jaehyeok¹,Kang Changwoo¹,Joo Kyungdon¹

Affiliation:

1. Artificial Intelligence Graduate School, UNIST

Funder

Institute of Information & communications Technology Planning & Evaluation (IITP)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203204.pdf?arnumber=10203204

Reference87 articles.

1. Learning representations and generative models for 3D point clouds;Achlioptas;ICML,2018

2. The ball-pivoting algorithm for surface reconstruction

3. Shapenet: An information-rich 3d model repository;Chang;arXiv preprint,2015

4. Wavegrad: Estimating gradients for waveform generation;Chen;ar Xiv preprint,2020

5. Wavegrad: Estimating gradients for waveform generation;Chen;ICLR,2021

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1. Explicit 3D reconstruction from images with dynamic graph learning and rendering-guided diffusion;Neurocomputing;2024-10

2. Tight Motion Planning by Riemannian Optimization for Sliding and Rolling with Finite Number of Contact Points;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

3. PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

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