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Found 6 papers, 1 papers with code
Learning to Simulate Realistic LiDARs
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
MeshUDF: Fast and Differentiable Meshing of Unsigned Distance Field Networks
Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces.
DeepMesh: Differentiable Iso-Surface Extraction
Our key insight is that by reasoning on how implicit field perturbations impact local surface geometry, one can ultimately differentiate the 3D location of surface samples with respect to the underlying deep implicit field.
Sketch2Mesh: Reconstructing and Editing 3D Shapes from Sketches
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information.
UCLID-Net: Single View Reconstruction in Object Space
Most state-of-the-art deep geometric learning single-view reconstruction approaches rely on encoder-decoder architectures that output either shape parametrizations or implicit representations.
AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
We present a high-speed, energy-efficient Convolutional Neural Network (CNN) architecture utilising the capabilities of a unique class of devices known as analog Focal Plane Sensor Processors (FPSP), in which the sensor and the processor are embedded together on the same silicon chip.
