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Chengkun Li

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5 papers

2 author rows

TMLR Journal 2026 Journal Article

Amortized Bayesian Workflow

Chengkun Li
Aki Vehtari
Paul-Christian Bürkner
Stefan T. Radev
Luigi Acerbi
Marvin Schmitt

Bayesian inference often faces a trade-off between computational speed and sampling accuracy. We propose an adaptive workflow that integrates rapid amortized inference with gold-standard MCMC techniques to achieve a favorable combination of both speed and accuracy when performing inference on many observed datasets. Our approach uses principled diagnostics to guide the choice of inference method for each dataset, moving along the Pareto front from fast amortized sampling via generative neural networks to slower but guaranteed-accurate MCMC when needed. By reusing computations across steps, our workflow synergizes amortized and MCMC-based inference. We demonstrate the effectiveness of this integrated approach on several synthetic and real-world problems with tens of thousands of datasets, showing efficiency gains while maintaining high posterior quality.

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IROS Conference 2025 Conference Paper

Gaussian Splatting with Reflectance Regularization for Endoscopic Scene Reconstruction

Chengkun Li
Kai Chen 0028
Shi Qiu
Jason Ying-Kuen Chan
Qi Dou 0001

Endoscopic reconstruction plays a crucial role in surgical robotics. The dynamic lighting conditions and integrated camera-light source in endoscopic scenes create a distinct reconstruction challenge: shape ambiguity. To mitigate this, we propose a Gaussian Splatting (GS) based framework for endoscopic scene reconstruction, enhanced with reflectance regularization. We embed every 3D Gaussian point with physical reflective attributes and combine this representation with a physically based inverse rendering framework. By jointly training 3DGS for view synthesis with this reflectance regularization, we are able to attain high-quality geometry without changing the volume rendering pipeline. Our experiments demonstrate the superiority in both geometry representation and rendering performance compared to existing GS approaches, making it a practical solution for endoscopic applications. Project is available at: https://med-air.github.io/GSR2.

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TMLR Journal 2025 Journal Article

InkSight: Offline-to-Online Handwriting Conversion by Teaching Vision-Language Models to Read and Write

Blagoj Mitrevski
Arina Rak
Julian Schnitzler
Chengkun Li
Andrii Maksai
Jesse Berent
Claudiu Cristian Musat

Digital note-taking is gaining popularity, offering a durable, editable, and easily indexable way of storing notes in a vectorized form, known as digital ink. However, a substantial gap remains between this way of note-taking and traditional pen-and-paper note-taking, a practice that is still favored by a vast majority. Our work InkSight, aims to bridge the gap by empowering physical note-takers to effortlessly convert their work (offline handwriting) to digital ink (online handwriting), a process we refer to as derendering. Prior research on the topic has focused on the geometric properties of images, resulting in limited generalization beyond their training domains. Our approach combines reading and writing priors, allowing training a model in the absence of large amounts of paired samples, which are difficult to obtain. To our knowledge, this is the first work that effectively derenders handwritten text in arbitrary photos with diverse visual characteristics and backgrounds. Furthermore, it generalizes beyond its training domain into simple sketches. Our human evaluation reveals that 87% of the samples produced by our model on the challenging HierText dataset are considered as a valid tracing of the input image and 67% look like a pen trajectory traced by a human.

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TMLR Journal 2025 Journal Article

Stacking Variational Bayesian Monte Carlo

Francesco Silvestrin
Chengkun Li
Luigi Acerbi

Approximate Bayesian inference for models with computationally expensive, black-box likelihoods poses a significant challenge, especially when the posterior distribution is complex. Many inference methods struggle to explore the parameter space efficiently under a limited budget of likelihood evaluations. Variational Bayesian Monte Carlo (VBMC) is a sample-efficient method that addresses this by building a local surrogate model of the log-posterior. However, its conservative exploration strategy, while promoting stability, can cause it to miss important regions of the posterior, such as distinct modes or long tails. In this work, we introduce Stacking Variational Bayesian Monte Carlo (S-VBMC), a method that overcomes this limitation by constructing a robust, global posterior approximation from multiple independent VBMC runs. Our approach merges these local approximations through a principled and inexpensive post-processing step that leverages VBMC's mixture posterior representation and per-component evidence estimates. Crucially, S-VBMC requires no additional likelihood evaluations and is naturally parallelisable, fitting seamlessly into existing inference workflows. We demonstrate its effectiveness on two synthetic problems designed to challenge VBMC's exploration and two real-world applications from computational neuroscience, showing substantial improvements in posterior approximation quality across all cases. Our code is available as a Python package at https://github.com/acerbilab/svbmc.

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TMLR Journal 2024 Journal Article

Modular Quantization-Aware Training for 6D Object Pose Estimation

Saqib Javed
Chengkun Li
Andrew Lawrence Price
Yinlin Hu
Mathieu Salzmann

Edge applications, such as collaborative robotics and spacecraft rendezvous, demand efficient 6D object pose estimation on resource-constrained embedded platforms. Existing 6D object pose estimation networks are often too large for such deployments, necessitating compression while maintaining reliable performance. To address this challenge, we introduce Modular Quantization-Aware Training (MQAT), an adaptive and mixed-precision quantization-aware training strategy that exploits the modular structure of modern 6D object pose estimation architectures. MQAT guides a systematic gradated modular quantization sequence and determines module-specific bit precisions, leading to quantized models that outperform those produced by state-of-the-art uniform and mixed-precision quantization techniques. Our experiments showcase the generality of MQAT across datasets, architectures, and quantization algorithms. Additionally, we observe that MQAT quantized models can achieve an accuracy boost (>7% ADI-0.1d) over the baseline full-precision network while reducing model size by a factor of 4x or more. Project Page: https://saqibjaved1.github.io/MQAT_

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