I am a first-year Ph.D. student at Angela Dai’s 3D AI Laboratory, at the TU Munich. Currently, I am focusing on in-the-wild indoor scene understanding challenges, including language-grounded segmentation and long-tail recognition.

I received my Bachelor’s from TU Budapest, and my Master’s through a double degree program at TU Berlin and ELTE Budapest majoring Computer Science of Autonomous Systems. Previously, I worked on robotics and vehicle localization, then during my master’s on augmented reality, SLAM, and real-time semantic reconstruction challenges.


Language-Grounded Indoor 3D Semantic Segmentation in the Wild
Dávid Rozenberszki, Or Litany, Angela Dai
ArXiv 2022
paper | video | project page

We present the ScanNet200 benchmark, which studies 200-class 3D semantic segmentation – an order of magnitude more class categories than previous 3D scene understanding benchmarks. To address this challenging 3D segmentation task, we propose to guide 3D feature learning by anchoring it to the richly-structured text embedding space of CLIP for the semantic class labels. This results in improved 3D semantic segmentation across the large set of class categories.

3D Semantic Label Transfer in Human-Robot Collaboration
Dávid Rozenberszki, Gábor Sörös, Szilvia Szeier, András Lőrincz
ICCVW 2021
paper | code

We present a system for sharing and reusing 3D semantic information between multiple agents with different viewpoints. We first co-localize all agents in the same coordinate system. Next, we create a 3D dense semantic model of the space from human viewpoints close to real time. Finally, by re-rendering the model's semantic labels from the ground robots' own estimated viewpoints and sharing them over the network, we can give 3D semantic understanding to simpler agents.

Demo: Towards Universal User Interfaces for Mobile Robots
Dávid Rozenberszki, Gábor Sörös
Augmented Humans Conference 2021
paper | video | bibtex

We demonstrate the concept and a prototype of automatically generated virtual user interfaces for mobile robots. Human(s) and robot(s) are co-localized in the space via their own on-board navigation and shared spatial anchors. The robot sends the description of context-aware user interface elements to a head-mounted display, which renders the virtual widgets around and seemingly attached to the physical robot.

LOL: Lidar-only Odometry and Localization in 3D point cloud maps
Dávid Rozenberszki, András Majdik
ICRA 2020
paper | video | code

We deal with the problem of odometry and localization for Lidar-equipped vehicles driving in urban environments, where a premade target map exists to localize against. In our problem formulation, to correct the accumulated drift of the Lidar-only odometry we apply a place recognition method to detect geometrically similar locations between the online 3D point cloud and the a priori offline map. In the proposed system, we integrate a state-of-the-art Lidar-only odometry algorithm with a recently proposed 3D point segment matching method by complementing their advantages.


  • Seminar for 3D Machine Learning - Winter 2021, Technical University of Munich