15 citations · 31 across the 6 of their papers we have counts for
7 papers
Lookalike3D: Seeing Double in 3D
Chandan Yeshwanth, Angela Dai
3D object understanding and generation methods produce impressive results, yet they often overlook a pervasive source of information in real-world scenes: repeated objects. We intr…
TUM2TWIN: Introducing the Large-Scale Multimodal Urban Digital Twin Benchmark Dataset
Olaf Wysocki, Benedikt Schwab, Manoj Kumar Biswanath +31
Urban Digital Twins (UDTs) have become essential for managing cities and integrating complex, heterogeneous data from diverse sources. Creating UDTs involves challenges at multiple…
ExCap3D: Expressive 3D Scene Understanding via Object Captioning with Varying Detail
Chandan Yeshwanth, David Rozenberszki, Angela Dai
Generating text descriptions of objects in 3D indoor scenes is an important building block of embodied understanding. Existing methods do this by describing objects at a single lev…
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner +1
We present ScanNet++, a large-scale dataset that couples together capture of high-quality and commodity-level geometry and color of indoor scenes. Each scene is captured with a hig…
Directional Message Passing on Molecular Graphs via Synthetic Coordinates
Johannes Gasteiger, Chandan Yeshwanth, Stephan Günnemann
Graph neural networks that leverage coordinates via directional message passing have recently set the state of the art on multiple molecular property prediction tasks. However, the…
SceneFormer: Indoor Scene Generation with Transformers
Xinpeng Wang, Chandan Yeshwanth, Matthias Nießner
We address the task of indoor scene generation by generating a sequence of objects, along with their locations and orientations conditioned on a room layout. Large-scale indoor sce…