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Geonu Kim

7 accepted papers

2025

Dr. Splat: Directly Referring 3D Gaussian Splatting via Direct Language Embedding Registration

CVPR 2025highlight

We introduce Dr. Splat, a novel approach for open-vocabulary 3D scene understanding leveraging 3D Gaussian Splatting. Unlike existing language-embedded 3DGS methods, which rely on a rendering process, our method directly associates language-aligned CLIP embeddings with 3D Gaussians for holistic 3D s…

Cited by 1SourcePDFScholar
2024

FPRF: Feed-Forward Photorealistic Style Transfer of Large-Scale 3D Neural Radiance Fields

AAAI 2024technical

We present FPRF, a feed-forward photorealistic style transfer method for large-scale 3D neural radiance fields. FPRF stylizes large-scale 3D scenes with arbitrary, multiple style reference images without additional optimization while preserving multi-view appearance consistency. Prior arts required…

Cited by 5SourcePDFScholar
2024

Factorized Multi-Resolution HashGrid for Efficient Neural Radiance Fields: Execution on Edge-Devices

RA-L 2024

We introduce Fact-Hash, a novel parameter-encoding method for training on-device neural radiance fields. Neural Radiance Fields (NeRF) have proven pivotal in 3D representations, but their applications are limited due to large computational resources. On-device training can open large application fie

Cited by 2SourceScholar
2024

ParamISP: Learned Forward and Inverse ISPs using Camera Parameters

CVPR 2024poster

RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated enabling physically-meaningful RAW-level image processing on input sRGB images. How…

2023

Benchmark of Machine Learning Force Fields for Semiconductor Simulations: Datasets, Metrics, and Comparative Analysis

NeurIPS 2023poster

As semiconductor devices become miniaturized and their structures become more complex, there is a growing need for large-scale atomic-level simulations as a less costly alternative to the trial-and-error approach during development. Although machine learning force fields (MLFFs) can meet the accurac…

2023

Human Pose Estimation in Extremely Low-Light Conditions

CVPR 2023poster

We study human pose estimation in extremely low-light images. This task is challenging due to the difficulty of collecting real low-light images with accurate labels, and severely corrupted inputs that degrade prediction quality significantly. To address the first issue, we develop a dedicated camer…

2022

Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement

ECCV 2022poster

"Estimating 3D poses and shapes in the form of meshes from monocular RGB images is challenging. Obviously, it is more difficult than estimating 3D poses only in the form of skeletons or heatmaps. When interacting persons are involved, the 3D mesh reconstruction becomes more challenging due to the am…