Carlos Esteves
I am a Research Scientist at Google Research, NYC. Please visit my website for more details and complete list of publications.
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Scaling Spherical CNNs
Jean-Jacques Slotine
International Conference on Machine Learning (ICML) (2023)
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Spherical CNNs generalize CNNs to functions on the sphere, by using spherical convolutions as the main linear operation. The most accurate and efficient way to compute spherical convolutions is in the spectral domain (via the convolution theorem), but this is still much more costly than the usual planar convolutions. For this reason, applications of spherical CNNs have so far been limited to small problems that can be approached with low model capacity. In this work, we show how spherical CNNs can be scaled for much larger problems. To achieve this, we made critical improvements including an implementation of core operations to exploit hardware accelerator characteristics, introducing novel variants of common model components, and showing how to construct application-specific input representations that exploit the properties of our model. Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks.
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LU-NeRF: Scene and Pose Estimation by Synchronizing Local Unposed NeRFs
Zezhou Cheng
Varun Jampani
Subhransu Maji
International Conference on Computer Vision (ICCV) (2023)
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A critical obstacle preventing NeRF models from being deployed broadly in the wild is their reliance on accurate camera poses. Consequently, there is growing interest in extending NeRF models to jointly optimize camera poses and scene representation, which offers an alternative to offthe-shelf SfM pipelines which have well-understood failure modes. Existing approaches for unposed NeRF operate under limiting assumptions, such as a prior pose distribution or coarse pose initialization, making them less effective in a general setting. In this work, we propose a novel approach, LU-NeRF, that jointly estimates camera poses and neural radiance fields with relaxed assumptions on pose configuration. Our approach operates in a local-to-global manner, where we first optimize over local subsets of the data, dubbed “mini-scenes.” LU-NeRF estimates local pose and geometry for this challenging few-shot task. The mini-scene
poses are brought into a global reference frame through a robust pose synchronization step, where a final global optimization of pose and scene can be performed. We show our LU-NeRF pipeline outperforms prior attempts at unposed NeRF without making restrictive assumptions on the pose prior. This allows us to operate in the general SE(3) pose setting, unlike the baselines. Our results also indicate our model can be complementary to feature-based SfM
pipelines as it compares favorably to COLMAP on lowtexture and low-resolution images.
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ASIC: Aligning Sparse in-the-wild Image Collections
Kamal Gupta
Varun Jampani
Abhinav Shrivastava
International Conference on Computer Vision (ICCV) (2023)
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We present a method for joint alignment of sparse in-thewild image collections of an object category. Most prior works assume either ground-truth keypoint annotations or a large dataset of images of a single object category. However, neither of the above assumptions hold true for the longtail of the objects present in the world. We present a selfsupervised technique that directly optimizes on a sparse collection of images of a particular object/object category to obtain consistent dense correspondences across the collection. We use pairwise nearest neighbors obtained from deep features of a pre-trained vision transformer (ViT) model as noisy and sparse keypoint matches and make them dense and accurate matches by optimizing a neural network that jointly maps the image collection into a learned canonical grid. Experiments on CUB and SPair-71k benchmarks demonstrate that our method can produce globally consistent and higher quality correspondences across the image collection when compared to existing self-supervised methods. Code and other material will be made available at https://kampta.github.io/asic.
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Learning to Transform for Generalizable Instance-wise Invariance
Utkarsh Singhal
Stella Yu
International Conference on Compute Vision (2023)
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Computer vision research has long aimed to build systems that are robust to spatial transformations found in natural data. Traditionally, this is done using data augmentation
or hard-coding invariances into the architecture. However, too much or too little invariance can hurt, and the correct amount is unknown a priori and dependent on the instance. Ideally, the appropriate invariance would be learned from data and inferred at test-time.
We treat invariance as a prediction problem. Given any image, we use a normalizing flow to predict a distribution over transformations and average the predictions over them. Since this distribution only depends on the instance, we can align instances before classifying them and generalize invariance across classes. The same distribution can also be used to adapt to out-of-distribution poses. This normalizing flow is trained end-to-end and can learn a much larger range of transformations than Augerino and InstaAug. When used as data augmentation, our method shows accuracy and robustness gains on CIFAR 10, CIFAR10-LT, and TinyImageNet.
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Light Field Neural Rendering
Leonid Sigal
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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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. Methods based on geometric reconstruction need only sparse
views, but cannot accurately model non-Lambertian effects. We
introduce a model that combines the strengths and mitigates the
limitations of these two directions. By operating on a
four-dimensional representation of the light field, our model learns
to represent view-dependent effects accurately. By enforcing geometric
constraints during training and inference, the scene geometry is
implicitly learned from a sparse set of views. Concretely, we
introduce a two-stage transformer-based model that first aggregates
features along epipolar lines, then aggregates features along
reference views to produce the color of a target ray. Our model
outperforms the state-of-the-art on multiple forward-facing and 360◦
datasets, with larger margins on scenes with severe view-dependent
variations. Code and results can be found at light-field-neural-
rendering.github.io.
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Generalizable Patch-Based Neural Rendering
Leonid Sigal
European Conference on Computer Vision (2022) (to appear)
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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. The recent focus has been on models
that overfit to a single scene, and the few attempts to learn models
that can synthesize novel views of unseen scenes mostly consist of
combining deep convolutional features with a NeRF-like model. We
propose a different paradigm, where no deep visual features and no
NeRF-like volume rendering are needed. Our method is capable of
predicting the color of a target ray in a novel scene directly, just
from a collection of patches sampled from the scene. We first leverage
epipolar geometry to extract patches along the epipolar lines of each
reference view. Each patch is linearly projected into a 1D feature
vector and a sequence of transformers process the collection. For
positional encoding, we parameterize rays as in a light field
representation, with the crucial difference that the coordinates are
canonicalized with respect to the target ray, which makes our method
independent of the reference frame and improves generalization. We
show that our approach outperforms the state-of-the-art on novel view
synthesis of unseen scenes even when being trained with considerably
less data than prior work. Our code is available at
https://mohammedsuhail.net/gen_patch_neural_rendering.
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Preview abstract
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. To this end, we introduce a method to
estimate arbitrary, non-parametric distributions on SO(3). Our key
idea is to represent the distributions implicitly, with a neural
network that estimates the probability given the input image and a
candidate pose. Grid sampling or gradient ascent can be used to find
the most likely pose, but it is also possible to evaluate the
probability at any pose, enabling reasoning about symmetries and
uncertainty. This is the most general way of representing
distributions on manifolds, and to showcase the rich expressive power,
we introduce a dataset of challenging symmetric and nearly-symmetric
objects. We require no supervision on pose uncertainty – the model
trains only with a single pose per example. Nonetheless, our implicit
model is highly expressive to handle complex distributions over 3D
poses, while still obtaining accurate pose estimation on standard
non-ambiguous environments, achieving state-of-the-art performance
on Pascal3D+ and ModelNet10-SO(3) benchmarks. Code, data, and
visualizations may be found at implicit-pdf.github.io.
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Spin-Weighted Spherical CNNs
Kostas Daniilidis
Advances in Neural Information Processing Systems (NeurIPS) 2020
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Learning equivariant representations is a promising way to reduce sample and model complexity and improve the generalization performance of deep neural networks. The spherical CNNs are successful examples, producing SO(3)-equivariant representations of spherical inputs. There are two main types of spherical CNNs. The first type lifts the inputs to functions on the rotation group SO(3) and applies convolutions on the group, which are computationally expensive since SO(3) has one extra dimension. The second type applies convolutions directly on the sphere, which are limited to zonal (isotropic) filters, and thus have limited expressivity. 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. The key idea is to consider spin-weighted spherical functions, which were introduced in physics in the study of gravitational waves. These are complex-valued functions on the sphere whose phases change upon rotation. We define a convolution between spin-weighted functions and build a CNN based on it. The spin-weighted functions can also be interpreted as spherical vector fields, allowing applications to tasks where the inputs or outputs are vector fields. Experiments show that our method outperforms previous methods on tasks like classification of spherical images, classification of 3D shapes and semantic segmentation of spherical panoramas.
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An Analysis of SVD for Deep Rotation Estimation
Jake Levinson
Arthur Chen
Angjoo Kanazawa
Advances in Neural Information Processing Systems (NeurIPS) 2020
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Symmetric orthogonalization via SVD, and closely related procedures, are well-known techniques for projecting matrices onto O(n) or SO(n). These tools have long been used for applications in computer vision, for example optimal 3D alignment problems solved by orthogonal Procrustes, rotation averaging, or Essential matrix decomposition. Despite its utility in different settings, SVD orthogonalization as a procedure for producing rotation matrices is typically overlooked in deep learning models, where the preferences tend toward classic representations like unit quaternions, Euler angles, and axis-angle, or more recently-introduced methods. Despite the importance of 3D rotations in computer vision and robotics, a single universally effective representation is still missing. Here, we explore the viability of SVD orthogonalization for 3D rotations in neural networks. We present a theoretical analysis of SVD as used for projection onto the rotation group. Our extensive quantitative analysis shows simply replacing existing representations with the SVD orthogonalization procedure obtains state of the art performance in many deep learning applications covering both supervised and unsupervised training.
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Cross-Domain 3D Equivariant Image Embeddings
Zhengyi Luo
Kostas Daniilidis
Proceedings of the 36th International Conference on Machine Learning, 2019, PMLR
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Spherical convolutional neural networks have been introduced recently as a tool to learn powerful feature representations of 3D shapes. Since spherical convolutions are equivariant to 3D rotations, the latent space of a SphericalCNN provides a natural representation for applications where 3D data may be observed in arbitrary orientations.
In this paper we explore if it is possible to learn 2D image embeddings with a similar equivariant structure: embedding the image of a 3D object should commute with rotations of the object. Our proposal is to bootstrap our model with supervision from a Spherical CNN pretrained with 3D shapes. Given an equivariant latent representation for 3D shapes, we introduce a novel supervised cross-domain embedding architecture that learns to map 2D images into the Spherical CNN's latent space. Our model is only optimized to produce the embeddings from an image's corresponding 3D shape. The trained model learns to encode images with 3D shape properties and is equivariant to 3D rotations of the observed object.
We show that learning only a rich embedding for images with appropriate geometric structure is in and of itself sufficient for tackling numerous applications. We show evidence from two different applications, relative pose estimation and novel view synthesis. In both settings we demonstrate that equivariant embeddings are sufficient for the application without requiring any task-specific supervised training.
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Labeling Panoramas with Spherical Hourglass Networks
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Kostas Daniilidis
360-degree Perception and Interaction Workshop at ECCV18 (2018)
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