Ruofan Liang
 (若凡, RF)

  Ph.D. Student
  University of Toronto
  ruofan [at]



I am a 3rd-year PhD student at the University of Toronto, supervised by Prof. Nandita Vijaykumar. Concurrently, I am also affiliated with the Vector Institute. I am current work as a research intern at Nvidia Toronto AI Lab.

Prior to my Ph.D., I received my Bachelor's degree from the Department of Computer Science, Shanghai Jiao Tong University (SJTU), where I worked with Prof. Quanshi Zhang and Prof. Jingwen Leng. I had a wonderful exchange semester in 2019 with research internship at National University of Singapore, advised by Prof. Bingsheng He.

My current research interests are in computer vision and graphics, particularly in efficient 3D scene learning tasks, neural field representations, and SLAM systems.


  • New [Dec, 2022] Passed PhD Qualifying Oral Exam🎉. Special thanks for the suggestions and feedbacks from Prof. David Lindell and Prof. Alec Jacobson



ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting
Ruofan Liang, Huiting Chen, Chunlin Li, Fan Chen, Selvakumar Panneer, Nandita Vijaykumar
ICCV 2023 (Oral)
[Project Page


SPIDR: SDF-based Neural Point Fields for Illumination and Deformation
Ruofan Liang, Jiahao Zhang, Haoda Li, Chen Yang, Yushi Guan, Nandita Vijaykumar
CVPRW 2023
[Project Page


CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
Ruofan Liang, Hongyi Sun, Nandita Vijaykumar
ICLR 2022
[Paper]  [Slides]  [Code


Knowledge Consistency between Neural Networks and Beyond
Ruofan Liang*, Tianlin Li*, Longfei Li, Jing Wang, Quanshi Zhang
ICLR 2020 (* equal contribution)
[Paper]  [Code

Previous Projects


A general programming framework towards the efficient implementation of neural radiance fields (NeRF) and its variants (e.g., NSVF, VolSDF, NeuS).
JIT compilation + multi-GPU training
Jan, 2022


A FPGA CAD tool that maps the logic RAMs required by the circuit to the physical RAMs with circuit area on FPGA as small as possible.
1st place in the competition of Prof. Vaughn Betz's FPGA course (ECE1756).
Nov, 2021


A RL-based Tetris playing agent. A model-based value iteration algorithm is proposed to make AI to play Tetris with promising performance (1000+ lines per game).
UofT CSC2515 Course Project, Dec, 2020


Accel-Video Pipe (AVPipe)
AVPipe is an integrated C++ library for AI video inference tasks on customers' devices, aiming to provide easily-used and high-performance experience.
3rd prize of the excellent bachelor thesis @ SJTU
Jun, 2020


I am a geek always excited to discover something Fun. Now, I am still on my long long way to obtaining knowledge & experience, hoping to exploit my potential.


Put on a happy face🙃~

Last updated: Sept, 2023