Shaohui Liu

Contact: "b1ueber2y at gmail dot com" [Curriculum Vitae]

Home About Me Research Why Blueberry

I am currently a third-year PhD student in the at advised by Prof. . My research primarily focuses on accurate localization/positioning and mapping, with an emphasis on multi-view geometry, robust estimation, and optimization. In some cases, these methods are further enhanced by deep learning and foundation models. Over the past two years, I have worked on large-scale geometric problems with multi-level structural features and sensor fusion.

I have also had the oppportunity to gain practical experience through industrial internships at , , , and .

Selected Projects [Full list]

Robust Incremental Structure-from-Motion with Hybrid Features

Shaohui Liu*, *, *, , , ,

ECCV, 2024.

[arXiv] [Paper] [Supplement] [Poster] [Code (as part of limap)] [BibTex]

Incremental SfM of points, lines, and vanshing points, with analytic covariance propagation.

limap: 3D Line Mapping Revisited

Shaohui Liu, , , ,

CVPR, 2023. Selected as a highlight paper.

[Project] [arXiv] [YouTube] [Paper] [Supplement] [Poster] [RSIP] [Slides] [Code] [BibTex]

An open-sourced system that robustly and efficiently constructs 3D line maps from multi-view imagery benefits multiple downstream applications such as visual localization and hybrid bundle adjustment, while also being flexible to adaptation of different 2D line detectors and matchers.

ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras

, Shaohui Liu, , ,

ECCV, 2022. Part of my intern project at .

[Project] [arXiv] [YouTube] [Slides] [Code] [BibTex]

Connecting, optimizing dense point trajectories from pairwise optical flows and segmenting them into labeled point tracks benefit an effective global structure-from-motion system for dynamic in-the-wild videos (image sequences) that exhibit complex foreground motions.

NerfingMVS: Guided Optimization of NeRF for Indoor Multi-view Stereo

, Shaohui Liu, , , ,

ICCV, 2021 / T-PAMI, 2023. Accepted as an Oral.

[Project] [arXiv] [Supplement] [YouTube] [Code] [BibTex]

Integrating conventional SfM + MVS reconstruction and learning-based depth priors largely improves the learned geometry of neural radiance fields (NeRF) in general indoor scenes, leading to significantly better multi-view depth estimation and novel view synthesis.

DIST: Rendering with Differentiable Sphere Tracing

Shaohui Liu, , , , ,

CVPR, 2020. Most work was done at .

[Project] [arXiv] [Supplement] [YouTube] [1-min Teaser] [Poster] [Slides] [Code] [BibTex]

A new differentiable sphere tracing algorithm enables systems to efficiently render various 2D observations from deep implicit signed distance functions and effectively perform robust inverse geometric optimization over real-world images with great generalization capability.

Towards Better Generalization: Joint Depth-Pose Learning without PoseNet

, Shaohui Liu, ,

CVPR, 2020.

[arXiv] [Supplement] [Slides] [Code] [BibTex]

Instead of relying on a PoseNet-like architecture, explicitly solving relative pose from optical flow correspondence improves the performance and generalization of self-supervised joint depth-pose learning methods on multiple challenging scenarios.

RepPoints: Point Set Representation for Object Detection

*, Shaohui Liu*, , ,

ICCV, 2019. Intern project at .

[arXiv] [Supplement] [Slides] [Blog] [Code] [BibTex]

Using a set of representative points to connect stages for simultaneous semantically aligned feature extraction and flexible geometric 2D representation produces a brand new effective object recognition framework without the need of anchors and bounding boxes.

Project August: Efficient Face Tracking at More than 1k FPS on CPU

Major project developer at in 2017.

[Project] [YouTube]

By caching, reusing and sharing intermediate features of a lightweight regression-based tracking model across frames we achieved extremely efficient face tracking and deployed the system onto real-world market products, as a preprocessing step for subsequent face analysis.