no code implementations • 14 Dec 2023 • Thomas W. Mitchel, Carlos Esteves, Ameesh Makadia
We introduce a framework for intrinsic latent diffusion models operating directly on the surfaces of 3D shapes, with the goal of synthesizing high-quality textures.
1 code implementation • ICCV 2023 • Utkarsh Singhal, Carlos Esteves, Ameesh Makadia, Stella X. Yu
However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance.
no code implementations • ICCV 2023 • Zezhou Cheng, Carlos Esteves, Varun Jampani, Abhishek Kar, Subhransu Maji, Ameesh Makadia
Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to off-the-shelf SfM pipelines which have well-understood failure modes.
1 code implementation • 8 Jun 2023 • Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia
Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation.
no code implementations • ICCV 2023 • Kamal Gupta, Varun Jampani, Carlos Esteves, Abhinav Shrivastava, Ameesh Makadia, Noah Snavely, Abhishek Kar
We present a self-supervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection.
no code implementations • 18 Aug 2022 • Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal
We present a system for accurately predicting stable orientations for diverse rigid objects.
no code implementations • 21 Jul 2022 • Mohammed Suhail, Carlos Esteves, Leonid Sigal, Ameesh Makadia
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably.
1 code implementation • CVPR 2022 • Mohammed Suhail, Carlos Esteves, Leonid Sigal, Ameesh Makadia
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene.
no code implementations • 29 Sep 2021 • Carlos Esteves, Tianjian Lu, Mohammed Suhail, Yi-fan Chen, Ameesh Makadia
In this work, we generalize positional encoding with Fourier features to non-Euclidean manifolds.
1 code implementation • 29 Sep 2021 • Kieran A Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain \textit{inactive} factors of variation.
2 code implementations • 10 Jun 2021 • Kieran Murphy, Carlos Esteves, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
Single image pose estimation is a fundamental problem in many vision and robotics tasks, and existing deep learning approaches suffer by not completely modeling and handling: i) uncertainty about the predictions, and ii) symmetric objects with multiple (sometimes infinite) correct poses.
1 code implementation • CVPR 2021 • Kefan Chen, Noah Snavely, Ameesh Makadia
Modern deep learning techniques that regress the relative camera pose between two images have difficulty dealing with challenging scenarios, such as large camera motions resulting in occlusions and significant changes in perspective that leave little overlap between images.
no code implementations • CVPR 2021 • Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Kanazawa
We cast this as the problem of aligning a source 3D object to a target 3D object from the same object category.
1 code implementation • CVPR 2021 • Shangzhe Wu, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa
Recent works have shown exciting results in unsupervised image de-rendering -- learning to decompose 3D shape, appearance, and lighting from single-image collections without explicit supervision.
1 code implementation • CVPR 2022 • Kieran A. Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
We propose a novel algorithm that utilizes a weak form of supervision where the data is partitioned into sets according to certain inactive (common) factors of variation which are invariant across elements of each set.
no code implementations • 1 Jan 2021 • Kieran A Murphy, Varun Jampani, Srikumar Ramalingam, Ameesh Makadia
In this work, we operate in the setting where limited information is known about the data in the form of groupings, or set membership, and the task is to learn representations which isolate the factors of variation that are common across the groupings.
1 code implementation • ICCV 2021 • Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, Angjoo Kanazawa
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.
2 code implementations • NeurIPS 2020 • Jake Levinson, Carlos Esteves, Kefan Chen, Noah Snavely, Angjoo Kanazawa, Afshin Rostamizadeh, Ameesh Makadia
Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto $O(n)$ or $SO(n)$.
2 code implementations • NeurIPS 2020 • Carlos Esteves, Ameesh Makadia, Kostas Daniilidis
In this paper, we present a new type of spherical CNN that allows anisotropic filters in an efficient way, without ever leaving the spherical domain.
Ranked #20 on Semantic Segmentation on Stanford2D3D Panoramic
no code implementations • ICLR Workshop DeepDiffEq 2019 • Jared Quincy Davis, Krzysztof Choromanski, Jake Varley, Honglak Lee, Jean-Jacques Slotine, Valerii Likhosterov, Adrian Weller, Ameesh Makadia, Vikas Sindhwani
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation.
1 code implementation • 19 Mar 2020 • Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
no code implementations • 7 Jun 2019 • Jake Levinson, Avneesh Sud, Ameesh Makadia
Generative modeling of 3D shapes has become an important problem due to its relevance to many applications across Computer Vision, Graphics, and VR.
1 code implementation • 6 Dec 2018 • Carlos Esteves, Avneesh Sud, Zhengyi Luo, Kostas Daniilidis, Ameesh Makadia
This embedding encodes images with 3D shape properties and is equivariant to 3D rotations of the observed object.
no code implementations • 6 Sep 2018 • Carlos Esteves, Kostas Daniilidis, Ameesh Makadia
With the recent proliferation of consumer-grade 360{\deg} cameras, it is worth revisiting visual perception challenges with spherical cameras given the potential benefit of their global field of view.
no code implementations • CVPR 2018 • Or Litany, Alex Bronstein, Michael Bronstein, Ameesh Makadia
In this work, we propose a novel learning-based method for the completion of partial shapes.
3 code implementations • ECCV 2018 • Carlos Esteves, Christine Allen-Blanchette, Ameesh Makadia, Kostas Daniilidis
We address the problem of 3D rotation equivariance in convolutional neural networks.
no code implementations • 22 Nov 2016 • Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani
Motivated by applications in robotics and computer vision, we study problems related to spatial reasoning of a 3D environment using sublevel sets of polynomials.
no code implementations • CVPR 2014 • Mehmet Ersin Yumer, Won Chun, Ameesh Makadia
We present a novel co-segmentation method for textured 3D shapes.