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vision-reading-group's Introduction

Vision-Reading-Group

Table of Contents

Overview

Mission

  • Foster collaboration and knowledge exchange in various sub-fields of Computer Vision.
  • Explore and discuss cutting-edge research (SOTA) across different areas.
  • Provide a platform for brainstorming, peer review, and constructive criticism.
  • Develop reading papers & ideation as a habit or hobby.

Format

  • Online Sessions : Convenient for everyone.
  • Semi-Formal : Develop reading and discussion skills in a relaxed atmosphere.
  • Frequency : Weekly (optional for members to present every week).

Paper Selection

  • Two Papers per Session : Focus on foundational or influential papers relevant to different sub-fields.
  • Open Selection : Members can add interesting papers to a shared list (presenter not required to be the recommender).

Presentation

  • Collaborative Exploration : Brief on-the-spot reading and group discussion encouraged. Members are free to prepare beforehand as well.
  • Active Participation : Attendees should guide the presenter and contribute to understanding, not just ask questions.
  • Ideas Sandbox : Members can present their own ideas for feedback and discussion.

Additional Points

  • Minimal Commitment : While regular participation is encouraged, a minimum of two volunteers per week for paper presentations is preferred.
  • Constructive Environment : Promote open discussion, respectful debate, and supportive feedback.

Research Papers

3D Vision

3D Correspondence

  • FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent [Submitted 23 Apr 2024 arxiv] [Paper] [Demo] ~Continue next time

3D Generation

  • DreamFusion: Text-to-3D using 2D Diffusion [ICLR 23] [Paper] [Demo]
  • LucidDreaming: Controllable Object-Centric 3D Generation [Submitted 30 Nov 2023 arxiv] [Paper] [Demo] [Code]

3D Editing

  • Awesome NERF editing
  • GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting [Submitted 24 Nov 2023 arxiv] [Paper] [Demo] [Code] [Video]
  • Instruct-NeRF2NeRF: Editing 3D Scenes with Instructions [ICCV 23] [Paper] [Demo] [Code]
  • DreamBooth3D: Subject-Driven Text-to-3D Generation [ICCV 23] [Paper] [Demo]
  • DreamEditor: Text-Driven 3D Scene Editing with Neural Fields [SIGGRAPH 23] [Paper] [Demo] [Code]

3D Grounding

  • Language Conditioned Spatial Relation Reasoning for 3D Object Grounding [NerIPS 22] [Paper] [Demo] [Code]
  • 3D Concept Grounding on Neural Fields [NeurIPS 22] [Paper] [Code]
  • Multi-View Transformer for 3D Visual Grounding [CVPR 22] [Paper] [Code]

3D Inpainting

  • Breathing New Life into 3D Assets with Generative Repainting [ICCV 23] [Paper] [Demo] [Code]
  • RePaint: Inpainting using Denoising Diffusion Probabilistic Models [CVPR 22] [Paper] [Code]
  • Diffusion Probabilistic Models for Scene-Scale 3D Categorical Data [Submitted 2 Jan 23 arxiv] [Paper] [Code]

NERFs

  • NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis [Paper] [Demo] [Code] [Dataset]
  • D-NeRF: Neural Radiance Fields for Dynamic Scenes [CVPR 21] [Paper] [Demo] [Code]
  • Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields [ICCV 21] [Paper] [Code]

Diffusion

  • Understanding Diffusion Models: A Unified Perspective. by Calvin Luo [Explainer, arxiv] [Paper]
  • LatentPaint: Image Inpainting in Latent Space with Diffusion Models [WACV 24] [Paper] [Video]
  • DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation [CVPR 23] [Paper] [Demo] [Dataset] [Tutorial]

Distillation

  • Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion [CVPR 20] [Paper] [Code]

Survey Papers

Foundation Models

  • A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT [Submitted 18 Feb 23 arxiv] [Paper]

3D Vision

NERFs

  • NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review [Submitted 30 Nov 23 arxiv] [Paper]

Emboidied AI

  • A Survey of Embodied AI: From Simulators to Research Tasks [IEEE Txn 22] [Paper]

Adversarial

Data Poisoning

  • Just How Toxic is Data Poisoning? A Unified Benchmark for Backdoor and Data Poisoning Attacks [PMLR '21] [Paper]

Explainers

Diffusion

Understanding Diffusion Models: A Unified Perspective [Submitted 25 Aug 23 arxiv] [Paper]

vision-reading-group's People

Contributors

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