Shaohui Liu

Contact: "b1ueber2y at gmail dot com"       [Curriculum Vitae]
I am currently a PhD student in the at advised by Prof. . I am mainly interested in 3D computer vision, multi-view geometry and its applications to real-world practice.

I obtained my bachelor's degree from Department of Electronic Engineering, . From 2018 to 2019, I visited the at and remotely worked with Prof. . After that, I had an academic visit at CVG and started as a Direct Doctorate student in Fall 2020. I have also spent time at , , and .

   Selected Projects

3D Line Mapping Revisited

Shaohui Liu, , , ,

CVPR, 2023.         Selected as a highlight paper.

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 .

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.         Accepted as an Oral.

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 .

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.

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 .

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.

Normalized Diversification

Shaohui Liu*, *, ,

CVPR, 2019.        

A novel loss term computed over the normalized pairwise distance matrices of the latent vectors and the corresponding outputs enforces both active extrapolation and safer interpolation of the mapping, ameliorating the notorious mode collapse problem on various vision applications.

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

Major project developer at in 2017.

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.