58 citations · 241 across the 26 of their papers we have counts for
30 papers
Equivariant Ray Embeddings for Implicit Multi-View Depth Estimation
Yinshuang Xu, Dian Chen, Katherine Liu +4
Incorporating inductive bias by embedding geometric entities (such as rays) as input has proven successful in multi-view learning. However, the methods adopting this technique typi…
Incorporating dense metric depth into neural 3D representations for view synthesis and relighting
Arkadeep Narayan Chaudhury, Igor Vasiljevic, Sergey Zakharov +4
Synthesizing accurate geometry and photo-realistic appearance of small scenes is an active area of research with compelling use cases in gaming, virtual reality, robotic-manipulati…
View-Invariant Policy Learning via Zero-Shot Novel View Synthesis
Stephen Tian, Blake Wulfe, Kyle Sargent +4
Large-scale visuomotor policy learning is a promising approach toward developing generalizable manipulation systems. Yet, policies that can be deployed on diverse embodiments, envi…
ReFiNe: Recursive Field Networks for Cross-modal Multi-scene Representation
Sergey Zakharov, Katherine Liu, Adrien Gaidon +1
The common trade-offs of state-of-the-art methods for multi-shape representation (a single model "packing" multiple objects) involve trading modeling accuracy against memory and st…
DiffusionNOCS: Managing Symmetry and Uncertainty in Sim2Real Multi-Modal Category-level Pose Estimation
Takuya Ikeda, Sergey Zakharov, Tianyi Ko +5
This paper addresses the challenging problem of category-level pose estimation. Current state-of-the-art methods for this task face challenges when dealing with symmetric objects a…
Zero-Shot Multi-Object Scene Completion
Shun Iwase, Katherine Liu, Vitor Guizilini +4
We present a 3D scene completion method that recovers the complete geometry of multiple unseen objects in complex scenes from a single RGB-D image. Despite notable advancements in…
FSD: Fast Self-Supervised Single RGB-D to Categorical 3D Objects
Mayank Lunayach, Sergey Zakharov, Dian Chen +3
In this work, we address the challenging task of 3D object recognition without the reliance on real-world 3D labeled data. Our goal is to predict the 3D shape, size, and 6D pose of…
NeO 360: Neural Fields for Sparse View Synthesis of Outdoor Scenes
Muhammad Zubair Irshad, Sergey Zakharov, Katherine Liu +5
Recent implicit neural representations have shown great results for novel view synthesis. However, existing methods require expensive per-scene optimization from many views hence l…
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Stephen Tian, Yancheng Cai, Hong-Xing Yu +5
Learned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Curre…
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
Vitor Guizilini, Igor Vasiljevic, Jiading Fang +4
Differentiable volumetric rendering is a powerful paradigm for 3D reconstruction and novel view synthesis. However, standard volume rendering approaches struggle with degenerate ge…
CARTO: Category and Joint Agnostic Reconstruction of ARTiculated Objects
Nick Heppert, Muhammad Zubair Irshad, Sergey Zakharov +5
We present CARTO, a novel approach for reconstructing multiple articulated objects from a single stereo RGB observation. We use implicit object-centric representations and learn a…
Zero-1-to-3: Zero-shot One Image to 3D Object
Ruoshi Liu, Rundi Wu, Basile Van Hoorick +3
We introduce Zero-1-to-3, a framework for changing the camera viewpoint of an object given just a single RGB image. To perform novel view synthesis in this under-constrained settin…
ROAD: Learning an Implicit Recursive Octree Auto-Decoder to Efficiently Encode 3D Shapes
Sergey Zakharov, Rares Ambrus, Katherine Liu +1
Compact and accurate representations of 3D shapes are central to many perception and robotics tasks. State-of-the-art learning-based methods can reconstruct single objects but scal…
Photo-realistic Neural Domain Randomization
Sergey Zakharov, Rares Ambrus, Vitor Guizilini +2
Synthetic data is a scalable alternative to manual supervision, but it requires overcoming the sim-to-real domain gap. This discrepancy between virtual and real worlds is addressed…
ShAPO: Implicit Representations for Multi-Object Shape, Appearance, and Pose Optimization
Muhammad Zubair Irshad, Sergey Zakharov, Rares Ambrus +3
Our method studies the complex task of object-centric 3D understanding from a single RGB-D observation. As it is an ill-posed problem, existing methods suffer from low performance…
SpOT: Spatiotemporal Modeling for 3D Object Tracking
Colton Stearns, Davis Rempe, Jie Li +5
3D multi-object tracking aims to uniquely and consistently identify all mobile entities through time. Despite the rich spatiotemporal information available in this setting, current…
Multi-View Object Pose Refinement With Differentiable Renderer
Ivan Shugurov, Ivan Pavlov, Sergey Zakharov +1
This paper introduces a novel multi-view 6 DoF object pose refinement approach focusing on improving methods trained on synthetic data. It is based on the DPOD detector, which prod…
DPODv2: Dense Correspondence-Based 6 DoF Pose Estimation
Ivan Shugurov, Sergey Zakharov, Slobodan Ilic
We propose a three-stage 6 DoF object detection method called DPODv2 (Dense Pose Object Detector) that relies on dense correspondences. We combine a 2D object detector with a dense…
Neural Groundplans: Persistent Neural Scene Representations from a Single Image
Prafull Sharma, Ayush Tewari, Yilun Du +7
We present a method to map 2D image observations of a scene to a persistent 3D scene representation, enabling novel view synthesis and disentangled representation of the movable an…
Unsupervised Discovery and Composition of Object Light Fields
Cameron Smith, Hong-Xing Yu, Sergey Zakharov +4
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised di…
Multi-Frame Self-Supervised Depth with Transformers
Vitor Guizilini, Rares Ambrus, Dian Chen +2
Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearanc…
Autolabeling 3D Objects with Differentiable Rendering of SDF Shape Priors
Sergey Zakharov, Wadim Kehl, Arjun Bhargava +1
We present an automatic annotation pipeline to recover 9D cuboids and 3D shapes from pre-trained off-the-shelf 2D detectors and sparse LIDAR data. Our autolabeling method solves an…
3D Object Instance Recognition and Pose Estimation Using Triplet Loss with Dynamic Margin
Sergey Zakharov, Wadim Kehl, Benjamin Planche +2
In this paper, we address the problem of 3D object instance recognition and pose estimation of localized objects in cluttered environments using convolutional neural networks. Insp…
HomebrewedDB: RGB-D Dataset for 6D Pose Estimation of 3D Objects
Roman Kaskman, Sergey Zakharov, Ivan Shugurov +1
Among the most important prerequisites for creating and evaluating 6D object pose detectors are datasets with labeled 6D poses. With the advent of deep learning, demand for such da…