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Neural Scene Representation and Neural Rendering

Seminar – Fall Semester 2024

Instructor: Lingjie Liu

TAs: Chuhao Chen, Xuyi Meng



Organization  |  Content  |  Format  |  Resources


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


Topics



Paper Presenting Schedule Presentation
Tian et al., ICCV 2023
Presenter 1: Fengrui Tian
Kerbl et al., SIGGRAPH 2023 (Best Paper Award)
Huang et al., SIGGRAPH 2024
Presenter 1: Daniel Alexander
Presenter 2: Wentinn Liao
Presenter 1: Hungju Wang
Presenter 2: Qiao Feng
Barron et al., ICCV 2021 (Oral, Best Paper Honorable Mention)
Barron et al., CVPR 2022 (Oral Presentation)
Barron et al., ICCV 2023 (Oral Presentation, Best Paper Finalist)
Yu et al., CVPR 2024 (Best Student Paper Finalist)
Presenter 1: Matthew Leonard
Presenter 2: Yunzhou Song
Presenter 1: Chen Wang
Presenter 2: Wentinn Liao
Fischer et al., arXiv 2024
Yu et al., CVPR 2021
Presenter 1: Yiduo Hao
Presenter 2: Hungju Wang
Presenter: Jiahui Lei
Presenter 1: Lee Milburn
Presenter 2: Fengtui Tian
Presenter 1: Alex Radchenko
Presenter 2: Chen Wang
Chen et al., CVPR 2024
Presenter 1: Xiangyu Han
Presenter 2: Albert Wang
Presenter: Jiatao Gu
Presenter: Ruoshi Liu
Moenne-Loccoz et al., SIGGRAPH Asia 2024
Zhu et al., 3DV 2024 (Oral, Best Paper Honorable Mention)
Presenter 1: Lee Milburn
Presenter 2: Zainab Afolabi
Presenter: Guandao Yang
Presenter 1: Zainab Afolabi
Presenter 2: Erik Jagnandan
Presenter 1: Yihang Liu
Presenter 2: Matthew Leonard
Presenter 1: Linzhan Mou
Presenter 2: Albert Wang
Presenter 1: Boshu Lei
Presenter 2: Yunzhou Song
Presenter 1: Yihang Liu
Presenter 2: Carlos Lopez Garces
Presenter 1: Boshu Lei
Presenter 2: Xiangyu Han
Presenter 1: Erik Jagnandan
Presenter 2: Yiduo Hao
Yu et al., CoRL 2024
Presenter 1: William Liang
Presenter 2: William Liang
Presenter 1: Qiao Feng
Presenter 2: Linzhan Mou
Presenter 1: Carlos Lopez Garces
Presenter 2: Alex Radchenko
Presenter 1: Xuyi Meng & Chuhao Chen
Presenter 2: Daniel Alexander

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
Müller et al.
SIGGRAPH 2022 (Best Paper Award)
Barron et al.
ICCV 2021 (Oral, Best Paper Honorable Mention)
Barron et al.
CVPR 2022 (Oral Presentation)
Barron et al.
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.
Yu et al.
CVPR 2024 (Best Student Paper Finalist)
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.
Charatan et al.
CVPR 2024 (Oral)
(infers a 3D Gaussian scene from two input views in a single forward pass.)
[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]
Skorokhodov et al.
ICLR 2023 (Oral)
Klinghoffer et al.
CVPR 2024 (Oral, Best Paper Award Finalist)
Kerr et al.
ICCV 2023 (Oral)
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.