4 citations · 14 across the 16 of their papers we have counts for
20 papers
MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception
Guido Caccianiga, Sergey Prokudin, Yutong Chen +9
Large-scale training and refined optimization techniques have greatly improved sparse multi-view 3D reconstruction. Despite their relevance to surgery, such methods have never befo…
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
Joaquin Gajardo, Michele Volpi, Marko Mihajlovic +3
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intri…
GGPT: Geometry Grounded Point Transformer
Yutong Chen, Yiming Wang, Xucong Zhang +2
Recent feed-forward networks have achieved remarkable progress in sparse-view 3D reconstruction by predicting dense point maps directly from RGB images. However, they often suffer…
HART: Human Aligned Reconstruction Transformer
Xiyi Chen, Shaofei Wang, Marko Mihajlovic +3
We introduce HART, a unified framework for sparse-view human reconstruction. Given a small set of uncalibrated RGB images of a person as input, it outputs a watertight clothed mesh…
Multi-View 3D Point Tracking
Frano Rajič, Haofei Xu, Marko Mihajlovic +7
We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers…
Enhancing Orthopedic Surgical Training With Interactive Photorealistic 3D Visualization
Roni Lekar, Tatiana Gerth, Sergey Prokudin +7
Surgical training integrates several years of didactic learning, simulation, mentorship, and hands-on experience. Challenges include stress, technical demands, and new technologies…
SplatFormer: Point Transformer for Robust 3D Gaussian Splatting
Yutong Chen, Marko Mihajlovic, Xiyi Chen +3
3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significan…
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Deheng Zhang, Jingyu Wang, Shaofei Wang +4
In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enablin…
FreSh: Frequency Shifting for Accelerated Neural Representation Learning
Adam Kania, Marko Mihajlovic, Sergey Prokudin +2
Implicit Neural Representations (INRs) have recently gained attention as a powerful approach for continuously representing signals such as images, videos, and 3D shapes using multi…
SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Marko Mihajlovic, Sergey Prokudin, Siyu Tang +4
Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerg…
Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
Yan Zhang, Sergey Prokudin, Marko Mihajlovic +2
Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision, essential in enhancing applications related to scene reconstruction, motion trackin…
Creating a Digital Twin of Spinal Surgery: A Proof of Concept
Jonas Hein, Frédéric Giraud, Lilian Calvet +8
Surgery digitalization is the process of creating a virtual replica of real-world surgery, also referred to as a surgical digital twin (SDT). It has significant applications in var…
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
Xiyi Chen, Marko Mihajlovic, Shaofei Wang +2
Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work, w…
ResFields: Residual Neural Fields for Spatiotemporal Signals
Marko Mihajlovic, Sergey Prokudin, Marc Pollefeys +1
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in m…
Dynamic Point Fields
Sergey Prokudin, Qianli Ma, Maxime Raafat +2
Recent years have witnessed significant progress in the field of neural surface reconstruction. While the extensive focus was put on volumetric and implicit approaches, a number of…
HARP: Personalized Hand Reconstruction from a Monocular RGB Video
Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges +1
We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstr…
SMPLpix: Neural Avatars from 3D Human Models
Sergey Prokudin, Michael J. Black, Javier Romero
Recent advances in deep generative models have led to an unprecedented level of realism for synthetically generated images of humans. However, one of the remaining fundamental limi…
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Sebastian Gomez-Gonzalez, Sergey Prokudin, Bernhard Scholkopf +1
Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot syste…
Efficient Learning on Point Clouds with Basis Point Sets
Sergey Prokudin, Christoph Lassner, Javier Romero
With the increased availability of 3D scanning technology, point clouds are moving into the focus of computer vision as a rich representation of everyday scenes. However, they are…
Deep Directional Statistics: Pose Estimation with Uncertainty Quantification
Sergey Prokudin, Peter Gehler, Sebastian Nowozin
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging condit…