Building scalable 3D understanding with minimal supervision.

Computer Vision PhD Student at CTU Prague.

Winner of S23DR Challenge 2026

Secured 1st place at the CVPR 2026 Structured Semantic 3D Reconstruction Challenge, following our 2025 win.

About Me

I am a PhD student at the Visual Recognition Group, Czech Technical University, advised by Lukas Neumann.

My research focuses on Weakly-Supervised 3D Detection for autonomous driving. I aim to reduce the reliance on expensive 3D annotations by leveraging temporal consistency and 2D cues from off-the-shelf vision models.

Previously, I completed my MSc with honours, receiving the Dean's Award for my thesis on 3D object detection.

2 Core A* Papers

1 Q1 Journal Paper

2x CVPR Challenge Winner

Selected Publications

Qualitative MonoSOWA++ car detections on the Waymo Open Dataset

IJCV · Accepted

MonoSOWA++: Universal autolabeling pipeline for Monocular 3D object detection

A universal pipeline for training monocular 3D object detectors without domain-specific human annotations. It supports camera-only and LiDAR weak supervision, aggregates data across different camera setups, and is approximately 500× faster than prior work.

Code

ICCV 2025

MonoSOWA: Scalable monocular 3D Object detector Without human Annotations

A novel approach enabling 3D object detection from a single RGB camera without human annotations. Introduces a Local Object Motion Model with ~700× speedup over prior work.

Paper Code

ECCV 2024

TCC-Det: Temporarily consistent cues for weakly-supervised 3D detection

Leveraging temporal consistency to train 3D detectors without 3D labels. Outperforms prior weakly-supervised methods on KITTI and Waymo datasets.

Paper Code Project Video