1 citations · 5 across the 12 of their papers we have counts for
13 papers
TwoSquared: 4D Generation from 2D Image Pairs
Lu Sang, Zehranaz Canfes, Dongliang Cao +3
Despite the astonishing progress in generative AI, 4D dynamic object generation remains an open challenge. With limited high-quality training data and heavy computing requirements,…
PRISM: Probabilistic Representation for Integrated Shape Modeling and Generation
Lei Cheng, Mahdi Saleh, Qing Cheng +4
Despite the advancements in 3D full-shape generation, accurately modeling complex geometries and semantics of shape parts remains a significant challenge, particularly for shapes w…
4Deform: Neural Surface Deformation for Robust Shape Interpolation
Lu Sang, Zehranaz Canfes, Dongliang Cao +3
Generating realistic intermediate shapes between non-rigidly deformed shapes is a challenging task in computer vision, especially with unstructured data (e.g., point clouds) where…
Implicit Neural Surface Deformation with Explicit Velocity Fields
Lu Sang, Zehranaz Canfes, Dongliang Cao +2
In this work, we introduce the first unsupervised method that simultaneously predicts time-varying neural implicit surfaces and deformations between pairs of point clouds. We propo…
Beyond Complete Shapes: A Benchmark for Quantitative Evaluation of 3D Shape Surface Matching Algorithms
Viktoria Ehm, Nafie El Amrani, Yizheng Xie +9
Finding correspondences between 3D deformable shapes is an important and long-standing problem in geometry processing, computer vision, graphics, and beyond. While various shape ma…
DiffCD: A Symmetric Differentiable Chamfer Distance for Neural Implicit Surface Fitting
Linus Härenstam-Nielsen, Lu Sang, Abhishek Saroha +2
Neural implicit surfaces can be used to recover accurate 3D geometry from imperfect point clouds. In this work, we show that state-of-the-art techniques work by minimizing an appro…
Coloring the Past: Neural Historical Buildings Reconstruction from Archival Photography
David Komorowicz, Lu Sang, Ferdinand Maiwald +1
Historical buildings are a treasure and milestone of human cultural heritage. Reconstructing the 3D models of these building hold significant value. The rapid development of neural…
Erasing the Ephemeral: Joint Camera Refinement and Transient Object Removal for Street View Synthesis
Mreenav Shyam Deka, Lu Sang, Daniel Cremers
Synthesizing novel views for urban environments is crucial for tasks like autonomous driving and virtual tours. Compared to object-level or indoor situations, outdoor settings pres…
Enhancing Surface Neural Implicits with Curvature-Guided Sampling and Uncertainty-Augmented Representations
Lu Sang, Abhishek Saroha, Maolin Gao +1
Neural implicit representations have become a popular choice for modeling surfaces due to their adaptability in resolution and support for complex topology. While previous works ha…
High-Quality RGB-D Reconstruction via Multi-View Uncalibrated Photometric Stereo and Gradient-SDF
Lu Sang, Bjoern Haefner, Xingxing Zuo +1
Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to re…
Gradient-SDF: A Semi-Implicit Surface Representation for 3D Reconstruction
Christiane Sommer, Lu Sang, David Schubert +1
We present Gradient-SDF, a novel representation for 3D geometry that combines the advantages of implict and explicit representations. By storing at every voxel both the signed dist…
Dive into Layers: Neural Network Capacity Bounding using Algebraic Geometry
Ji Yang, Lu Sang, Daniel Cremers
The empirical results suggest that the learnability of a neural network is directly related to its size. To mathematically prove this, we borrow a tool in topological algebra: Bett…
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational Approach
Lu Sang, Bjoern Haefner, Daniel Cremers
A novel approach towards depth map super-resolution using multi-view uncalibrated photometric stereo is presented. Practically, an LED light source is attached to a commodity RGB-D…