Tanner Schmidt
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Segment This Thing: Foveated Tokenization for Efficient Point-Prompted Segmentation Open
This paper presents Segment This Thing (STT), a new efficient image segmentation model designed to produce a single segment given a single point prompt. Instead of following prior work and increasing efficiency by decreasing model size, we…
ERF: Explicit Radiance Field Reconstruction From Scratch Open
We propose a novel explicit dense 3D reconstruction approach that processes a set of images of a scene with sensor poses and calibrations and estimates a photo-real digital model. One of the key innovations is that the underlying volumetri…
Identity-Disentangled Neural Deformation Model for Dynamic Meshes Open
Neural shape models can represent complex 3D shapes with a compact latent space. When applied to dynamically deforming shapes such as the human hands, however, they would need to preserve temporal coherence of the deformation as well as th…
View article: Neural 3D Video Synthesis from Multi-view Video
Neural 3D Video Synthesis from Multi-view Video Open
We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpol…
View article: STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering
STaR: Self-supervised Tracking and Reconstruction of Rigid Objects in Motion with Neural Rendering Open
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are surprisi…
FroDO: From Detections to 3D Objects Open
Object-oriented maps are important for scene understanding since they jointly capture geometry and semantics, allow individual instantiation and meaningful reasoning about objects. We introduce FroDO, a method for accurate 3D reconstructio…
View article: PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Open
Estimating the 6D pose of known objects is important for robots to interact with the real world.The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects.…
PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes Open
Estimating the 6D pose of known objects is important for robots to interact with the real world. The problem is challenging due to the variety of objects as well as the complexity of a scene caused by clutter and occlusions between objects…
Dynamic High Resolution Deformable Articulated Tracking Open
The last several years have seen significant progress in using depth cameras for tracking articulated objects such as human bodies, hands, and robotic manipulators. Most approaches focus on tracking skeletal parameters of a fixed shape mod…