NeRF: Representing Scenes as Neural Radiance Fields for View
Synthesis ECCV, 2020 |
NeuS: Learning Neural
Implicit
Surfaces by Volume Rendering for Multi-view Reconstruction NeurIPS, 2021 |
Paper Presenting Schedule | Presentation |
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Presenter 1: Fengrui Tian
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Presenter 1: Daniel Alexander
Presenter 2: Wentinn Liao
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Presenter 1: Hungju Wang
Presenter 2: Qiao Feng
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Presenter 1: Matthew Leonard
Presenter 2: Yunzhou Song
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Presenter 1: Chen Wang
Presenter 2: Wentinn Liao
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Presenter 1: Yiduo Hao
Presenter 2: Hungju Wang
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Presenter: Jiahui Lei
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Presenter 1: Lee Milburn
Presenter 2: Fengtui Tian
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Presenter 1: Alex Radchenko
Presenter 2: Chen Wang
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Presenter 1: Xiangyu Han
Presenter 2: Albert Wang
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Presenter: Jiatao Gu
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Presenter: Ruoshi Liu
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Presenter 1: Lee Milburn
Presenter 2: Zainab Afolabi
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Presenter: Guandao Yang
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Presenter 1: Zainab Afolabi
Presenter 2: Erik Jagnandan
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Presenter 1: Yihang Liu
Presenter 2: Matthew Leonard
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Presenter 1: Linzhan Mou
Presenter 2: Albert Wang
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Presenter 1: Boshu Lei
Presenter 2: Yunzhou Song
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Presenter 1: Yihang Liu
Presenter 2: Carlos Lopez Garces
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Presenter 1: Boshu Lei
Presenter 2: Xiangyu Han
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Presenter 1: Erik Jagnandan
Presenter 2: Yiduo Hao
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Presenter 1: William Liang
Presenter 2: William Liang
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Presenter 1: Qiao Feng
Presenter 2: Linzhan Mou
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Presenter 1: Carlos Lopez Garces
Presenter 2: Alex Radchenko
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Presenter 1: Xuyi Meng & Chuhao Chen
Presenter 2: Daniel Alexander
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The following table lists all the topics and papers that will be discussed in the seminar. Once every participant has submitted their choice of topics and papers, the previous list will be updated to show the presenter of each topic. Send us an email if you cannot access a paper for some reason.
Click on each topic to show the papers to be discussed or show all papers .
Topic and Papers |
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SIGGRAPH 2023 (Best Paper Award)
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SIGGRAPH 2022 (Best Paper Award)
ECCV 2022
SIGGRAPH 2023
ICCV 2021 (Oral)
CVPR 2022 (Oral)
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ICCV 2021 (Oral, Best Paper Honorable Mention)
CVPR 2022 (Oral Presentation)
ICCV 2023 (Oral Presentation, Best Paper Finalist)
Mip-NeRF v.s. Mip-NeRF 360 v.s. Zip-NeRF:
Common: Address the aliasing artifacts of NeRF. Mip-NeRF: Mitigates aliasing artifacts at different resolutions by replacing point sampling with Gaussian sampling. Mip-NeRF 360: Extends Mip-NeRF to unbounded scenes using a non-linear scene parameterization to allocate appropriate capacity for foreground and background. Zip-NeRF: Addresses z-aliasing artifacts from Mip-NeRF 360's resampling and adapts to an efficient grid representation using multisampling within a conical frustum. CVPR 2024 (Best Student Paper Finalist)
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SIGGRAPH 2023
SIGGRAPH 2024 (Best Paper Honorable Mention)
MERF v.s. SMERF:
Common: Use compact representation to achieve high-quality real-time volumetric rendering. MERF: Proposed a combination of a low-resolution 3D grid and a set of higher-resolution 2D planes. SMERF: Supports real-time rendering on mobile devices; dedicates each viewpoint a MERF for large scenes. arXiv 2024
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CVPR 2024 (Oral)
(infers a 3D Gaussian scene from two input views in a single forward pass.)
ICLR 2024 (Oral)
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[Per-scene optimization: diffusion distillation]
[Single-view image → Multi-view image → 3D reconstruction]
[Pose-free 3D Generation]
PF-LRM v.s. SpaRP:
Common: 3D reconstruction from sparse unknown-posed images. PF-LRM: Explicit matching through pointcloud + differentiable PnP solver. SpaRP: Distill stable diffusion model to predict NOCS images for camera pose estimation. [Native 3D Generation]
[Multi-view ImageNet]
ICLR 2023 (Oral)
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ICCV 2023 (Oral)
CVPR 2024 (Oral, Best Paper Award Finalist)
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ICCV 2023 (Oral)
CORL 2023 (Best Paper Finalist)
LERF v.s. LERF-TOGO:
Common: Embed language embeddings into 3D scene representation. LERF: Enables pixel-aligned zero-shot queries on the distilled 3D CLIP embedding. LERF-TOGO: Extends LERF to task-oriented grasping by adding DINO feature grouping. |
NeurIPS 2021
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