152 citations · 277 across the 12 of their papers we have counts for
14 papers
SPEAR: A Simulator for Photorealistic Embodied AI Research
Mike Roberts, Renhan Wang, Rushikesh Zawar +10
Interactive simulators have become powerful tools for training embodied agents and generating synthetic visual data, but existing photorealistic simulators suffer from limited gene…
Sharp Monocular View Synthesis in Less Than a Second
Lars Mescheder, Wei Dong, Shiwei Li +10
We present SHARP, an approach to photorealistic view synthesis from a single image. Given a single photograph, SHARP regresses the parameters of a 3D Gaussian representation of the…
CoMotion: Concurrent Multi-person 3D Motion
Alejandro Newell, Peiyun Hu, Lahav Lipson +2
We introduce an approach for detecting and tracking detailed 3D poses of multiple people from a single monocular camera stream. Our system maintains temporally coherent predictions…
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second
Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain +4
We present a foundation model for zero-shot metric monocular depth estimation. Our model, Depth Pro, synthesizes high-resolution depth maps with unparalleled sharpness and high-fre…
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting
Benjamin Ummenhofer, Sanskar Agrawal, Rene Sepulveda +6
Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not onl…
Unsupervised Contrastive Domain Adaptation for Semantic Segmentation
Feihu Zhang, Vladlen Koltun, Philip Torr +2
Semantic segmentation models struggle to generalize in the presence of domain shift. In this paper, we introduce contrastive learning for feature alignment in cross-domain adaptati…
Dancing under the stars: video denoising in starlight
Kristina Monakhova, Stephan R. Richter, Laura Waller +1
Imaging in low light is extremely challenging due to low photon counts. Using sensitive CMOS cameras, it is currently possible to take videos at night under moonlight (0.05-0.3 lux…
DeepMesh: Differentiable Iso-Surface Extraction
Benoit Guillard, Edoardo Remelli, Artem Lukoianov +4
Geometric Deep Learning has recently made striking progress with the advent of continuous deep implicit fields. They allow for detailed modeling of watertight surfaces of arbitrary…
Enhancing Photorealism Enhancement
Stephan R. Richter, Hassan Abu AlHaija, Vladlen Koltun
We present an approach to enhancing the realism of synthetic images. The images are enhanced by a convolutional network that leverages intermediate representations produced by conv…
MeshSDF: Differentiable Iso-Surface Extraction
Edoardo Remelli, Artem Lukoianov, Stephan R. Richter +4
Geometric Deep Learning has recently made striking progress with the advent of continuous Deep Implicit Fields. They allow for detailed modeling of watertight surfaces of arbitrary…
What Do Single-view 3D Reconstruction Networks Learn?
Maxim Tatarchenko, Stephan R. Richter, René Ranftl +3
Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research. All existing techniques are united by…
Matryoshka Networks: Predicting 3D Geometry via Nested Shape Layers
Stephan R. Richter, Stefan Roth
In this paper, we develop novel, efficient 2D encodings for 3D geometry, which enable reconstructing full 3D shapes from a single image at high resolution. The key idea is to pose…
Playing for Benchmarks
Stephan R. Richter, Zeeshan Hayder, Vladlen Koltun
We present a benchmark suite for visual perception. The benchmark is based on more than 250K high-resolution video frames, all annotated with ground-truth data for both low-level a…
Playing for Data: Ground Truth from Computer Games
Stephan R. Richter, Vibhav Vineet, Stefan Roth +1
Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extrem…