Self-Evolving Neural Radiance Fields

1 code implementation • 2 Dec 2023 • Jaewoo Jung, Jisang Han, Jiwon Kang, Seongchan Kim, Min-Seop Kwak, Seungryong Kim

We formulate few-shot NeRF into a teacher-student framework to guide the network to learn a more robust representation of the scene by training the student with additional pseudo labels generated from the teacher.

3D Reconstruction Novel View Synthesis

DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth Adaptation

1 code implementation • 30 May 2023 • Jiuhn Song, Seonghoon Park, Honggyu An, Seokju Cho, Min-Seop Kwak, SungJin Cho, Seungryong Kim

Employing monocular depth estimation (MDE) networks, pretrained on large-scale RGB-D datasets, with powerful generalization capability would be a key to solving this problem: however, using MDE in conjunction with NeRF comes with a new set of challenges due to various ambiguity problems exhibited by monocular depths.

Monocular Depth Estimation Novel View Synthesis

Let 2D Diffusion Model Know 3D-Consistency for Robust Text-to-3D Generation

1 code implementation • 14 Mar 2023 • Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Hyeonsu Kim, Jaehoon Ko, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, Seungryong Kim

Text-to-3D generation has shown rapid progress in recent days with the advent of score distillation, a methodology of using pretrained text-to-2D diffusion models to optimize neural radiance field (NeRF) in the zero-shot setting.

3D Generation Single-View 3D Reconstruction +1

GeCoNeRF: Few-shot Neural Radiance Fields via Geometric Consistency

1 code implementation • 26 Jan 2023 • Min-Seop Kwak, Jiuhn Song, Seungryong Kim

We present a novel framework to regularize Neural Radiance Field (NeRF) in a few-shot setting with a geometry-aware consistency regularization.