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Found 6 papers, 3 papers with code
Your Diffusion Model is Secretly a Zero-Shot Classifier
Our generative approach to classification, which we call Diffusion Classifier, attains strong results on a variety of benchmarks and outperforms alternative methods of extracting knowledge from diffusion models.
Ranked #1 on
Image Classification
on ObjectNet (ImageNet classes)
NeuralODF: Learning Omnidirectional Distance Fields for 3D Shape Representation
We propose Omnidirectional Distance Fields (ODFs), a new 3D shape representation that encodes geometry by storing the depth to the object's surface from any 3D position in any viewing direction.
Topologically-Aware Deformation Fields for Single-View 3D Reconstruction
The 3D shapes are generated implicitly as deformations to a category-specific signed distance field and are learned in an unsupervised manner solely from unaligned image collections and their poses without any 3D supervision.
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
Our goal is to significantly speed up the runtime of current state-of-the-art stereo algorithms to enable real-time inference.
