LABA@SNU

[AK @_akhaliq]

Click-Gaussian

Interactive Segmentation to Any 3D Gaussians

Interactive segmentation of 3D Gaussians opens a great opportunity for real-time manipulation of 3D scenes thanks to the real-time rendering capability of 3D Gaussian Splatting. However, the current methods suffer from time-consuming post-processing to deal with noisy segmentation output. Also, they struggle to provide detailed segmentation, which is important for fine-grained manipulation of 3D scenes. In this study, we propose Click-Gaussian, which learns distinguishable feature fields of two-level granularity, facilitating segmentation without time-consuming post-processing. We delve into challenges stemming from inconsistently learned feature fields resulting from 2D segmentation obtained independently from a 3D scene. 3D segmentation accuracy deteriorates when 2D segmentation results across the views, primary cues for 3D segmentation, are in conflict. To overcome these issues, we propose Global Feature-guided Learning (GFL). GFL constructs the clusters of global feature candidates from noisy 2D segments across the views, which smooths out noises when training the features of 3D Gaussians. Our method runs in 10 ms per click, 15 to 130 times as fast as the previous methods, while also significantly improving segmentation accuracy.

[MrNeRF @janusch_patas]

Click-Gaussian: Interactive Segmentation to Any 3D Gaussians
https://t.co/XLEFFwiRKO
Project: https://t.co/vhhdyt42wg
Method ⬇️ 1 | 2

[Seoul National University CALS @snucals1]

Exciting news! "Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields," authored by Prof. Taehyeong Kim and his co-workers, has been accepted for @ICCVConference

@snucalspress

https://t.co/ubZ0C6XueK

[LABA@SNU @tkim_snu]

This AI Paper Proposes Blending-NeRF that Consists of Pretrained NeRF and Editable NeRF for Text-Driven Localized 3D Object Editing https://t.co/I3vdOztZmN

[Seoul National University CALS @snucals1]

🎉 Exciting news! "Blending-NeRF: Text-Driven Localized Editing in Neural Radiance Fields," authored by Prof. Kim and his co-workers, has been accepted for #ICCV2023! 🌟

@SeoulNatlUni @snucalspress @ICCVConference

https://t.co/ubZ0C6XueK

[Robert Scoble @Scobleizer]

Metaverse 2.0: your 3D scenes can be edited by prompting.

[Seoul National University CALS @snucals1]

🍅 We present another recent research paper by Prof. Taehyeong Kim: "2D pose estimation of multiple tomato
fruit-bearing systems for robotic harvesting" 🌱🤖

@tkim_snu @snucalspress

https://t.co/vBilQrcWaG

[Seoul National University CALS @snucals1]

🌱 Overcoming the scarcity of labeled data in agriculture! Introducing our latest research paper by Prof. Taehyeong Kim: "Domain adapted broiler density map estimation using negative-patch data augmentation." 🐔📊💡

@tkim_snu @snucalspress

https://t.co/mjivsPK79A