no code implementations • 22 Sep 2022 • Benoit Guillard, Sai Vemprala, Jayesh K. Gupta, Ondrej Miksik, Vibhav Vineet, Pascal Fua, Ashish Kapoor
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
1 code implementation • 29 Nov 2021 • Benoit Guillard, Federico Stella, Pascal Fua
Unsigned Distance Fields (UDFs) can be used to represent non-watertight surfaces.
no code implementations • 20 Jun 2021 • Benoit Guillard, Edoardo Remelli, Artem Lukoianov, Stephan R. Richter, Timur Bagautdinov, Pierre Baque, Pascal Fua
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.
no code implementations • ICCV 2021 • Benoit Guillard, Edoardo Remelli, Pierre Yvernay, Pascal Fua
Reconstructing 3D shape from 2D sketches has long been an open problem because the sketches only provide very sparse and ambiguous information.
no code implementations • NeurIPS 2020 • Benoit Guillard, Edoardo Remelli, Pascal Fua
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.
no code implementations • 2 Jun 2020 • Matthew Z. Wong, Benoit Guillard, Riku Murai, Sajad Saeedi, Paul H. J. Kelly
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.