Re: 講演会(2/17 PM)のお知らせ

[Akihiro Sugimoto]

来週の月曜に迫りましたので、再送させていただきます。

杉本

On Mon, 03 Feb 2025 13:01:34 +0900
Akihiro Sugimoto <sugi...@nii.ac.jp> wrote:

> 各位
>
> 下記の講演会3件をハイブリッドで開催しますので、奮ってご参加ください。
>
> 杉本
> ------
> 日時:
> 2月17日(月曜日) 13h30-16h45
>
> 会場:
> 国立情報学研究所19F Room 1902&1903 及び
> 　　Zoom link:
> https://us02web.zoom.us/j/89003851193?pwd=ZiSceBggwoA0OKtPNOqbqA8os79
> wNG.1
> Meeting ID: 890 0385 1193
> Passcode: 381228
>
> -------------------- 1st talk -----------------------
> Speaker: Zuzana Kukelova (Czech Technical University in Prague)
> https://cmp.felk.cvut.cz/~kukelova/
>
> Title: A Brief Introduction to Camera Geometry Estimation Solvers
>
> Abstract:
> We will briefly introduce the most common camera geometry estimation
> problems, including relative and absolute pose problems for calibrated,
> uncalibrated, and partially calibrated cameras. Starting with a short
> historic overview, we will then discuss the current state-of-the-art for
> these problems. This includes highlighting the challenges faced when
> aiming for efficient and robust solutions for camera geometry estimation.
>
> -------------------- 2nd talk -----------------------
> Speaker: Torsten Sattler (Czech Technical University in Prague)
> https://tsattler.github.io/
>
> Title: 3D Reconstruction with Gaussian Splatting
>
> Abstract:
> Accurate 3D reconstruction is a core computer vision problem with
> many applications, including autonomous robots such as self-driving cars,
> cultural heritage documentation, and content creation for the
> entertainment industry (movies, games, etc.). Traditionally, 3D
> reconstructions have been based on 3D meshes and point clouds.
> Recently, learning-based approaches, such as neural radiance fields
> (NeRFs) and most recently 3D Gaussian Splatting (3DGS), have become
> popular. These representations are learned from images with known
> intrinsics and extrinsics and generate (close-to) photorealistic
> representations of scenes and objects. Compared to NeRFs, which
> can be slow to train and slow to render, 3DGS offers both faster
> training and test times. This talk first briefly reviews the original
> 3DGS formulation before identifying shortcomings and explaining how
> to resolve them. In particular, we will discuss (i) how to handle
> artifacts in the reconstruction caused by a limited set of training
> viewpoints, (ii) how to extend the original formulation for handling
> images taken under different conditions (day, night, etc.), and (iii)
> how to extract accurate 3D meshes from 3DGS representations by
> defining a field on top of the 3D Gaussians used to represent the scene.
> In addition, we will briefly mention ongoing efforts to ensure that
> benchmark results are comparable and that comparisons are fair.
>
> -------------------- 3rd talk -----------------------
> Speaker: Ming-Hsuan Yang (University of California, Merced/Google
> DeepMind)
> https://faculty.ucmerced.edu/mhyang/
>
> Title: Video Understanding and Generation with Multimodal Foundation Models
>
> Abstract:
> Recent advances in vision and language models have significantly
> improved visual understanding and generation tasks. In this talk, I will
> present our latest research on designing effective tokenizers for
> transformers and our efforts to adapt frozen large language models for
> diverse vision tasks. These tasks include visual classification, video-text
> retrieval, visual captioning, visual question answering, visual grounding,
> video generation, stylization, outpainting, and video-to-audio conversion.
> If time permits, I will also discuss our recent findings in dynamic 3D
> vision.
>
> ----------------------------------------------------------------------------------
>
>