Daoyi Gao

I am a PhD student at the 3D AI Lab at Technical University of Munich.

Previously, I graduated from Robotics, Cognition, Intelligence at TUM.

My research interests are in the field of 3D computer vision, CAD-driven scene understanding, and 3D generative modeling.

Email  /  Google Scholar /  Github /  LinkedIn

We proposed a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry.

We proposed a weakly-supervised approach for CAD model retrieval and alignment from an RGB image. Our approach utilzes diffusion models to tackle the ambiguities in the monocular perception, and achives robuts cross-domain performance while only trained on synthetic dataset

We proposed a hybrid model that utilizes polarizaiotn information with physical priors in a data-driven learning strategy to improve the accuracy of pose predictions for photometric challenging objects.

* Equal contribution. Alphabetical order.

We proposed a self-supervised monocular depth estimation pipeline that aims to improve consistency while preserving accuracy. We also proposed a new Temporal Consistency Metric (TCM) to quantify depth consistency across frames.

* Equal contribution. Order determined randomly.

We proposed a spatial-temporal attention mechanism guided by geometric constraints to aggregate geometrically meaningful attention and improve temporal depth stability and accuracy compared to previous methods.

* Equal contribution. Order determined randomly.

Credicts: Jon Barron