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Found 22 papers, 10 papers with code
Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research
We present Kaolin, a PyTorch library aiming to accelerate 3D deep learning research.
gradSLAM: Automagically differentiable SLAM
Blending representation learning approaches with simultaneous localization and mapping (SLAM) systems is an open question, because of their highly modular and complex nature.
SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM
Dense simultaneous localization and mapping (SLAM) is crucial for robotics and augmented reality applications.
AnyLoc: Towards Universal Visual Place Recognition
In this work, we develop a universal solution to VPR -- a technique that works across a broad range of structured and unstructured environments (urban, outdoors, indoors, aerial, underwater, and subterranean environments) without any re-training or fine-tuning.
Ranked #1 on
Visual Place Recognition
on Nardo-Air R
Follow Anything: Open-set detection, tracking, and following in real-time
We demonstrate FAn on a real-world robotic system (a micro aerial vehicle) and report its ability to seamlessly follow the objects of interest in a real-time control loop.
MonoLayout: Amodal scene layout from a single image
We dub this problem amodal scene layout estimation, which involves "hallucinating" scene layout for even parts of the world that are occluded in the image.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation.
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
DRACO: Weakly Supervised Dense Reconstruction And Canonicalization of Objects
We present DRACO, a method for Dense Reconstruction And Canonicalization of Object shape from one or more RGB images.
Multi-object Monocular SLAM for Dynamic Environments
In this paper, we tackle the problem of multibody SLAM from a monocular camera.
gradSim: Differentiable simulation for system identification and visuomotor control
We consider the problem of estimating an object's physical properties such as mass, friction, and elasticity directly from video sequences.
AutoLay: Benchmarking amodal layout estimation for autonomous driving
Given an image or a video captured from a monocular camera, amodal layout estimation is the task of predicting semantics and occupancy in bird's eye view.
Rethinking Optimization with Differentiable Simulation from a Global Perspective
In this work, we study the challenges that differentiable simulation presents when it is not feasible to expect that a single descent reaches a global optimum, which is often a problem in contact-rich scenarios.
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology.
Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares
We propose a differentiable nonlinear least squares framework to account for uncertainty in relative pose estimation from feature correspondences.
Tactile Estimation of Extrinsic Contact Patch for Stable Placement
In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation.
ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning
We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts.
STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent
In this paper, we propose a novel approach to TAMP that relaxes discrete-and-continuous TAMP problems into inference problems on a continuous domain.
Talk2BEV: Language-enhanced Bird's-eye View Maps for Autonomous Driving
Talk2BEV is a large vision-language model (LVLM) interface for bird's-eye view (BEV) maps in autonomous driving contexts.
Differentiable Visual Computing for Inverse Problems and Machine Learning
This approach is predicated by neural network differentiability, the requirement that analytic derivatives of a given problem's task metric can be computed with respect to neural network's parameters.
Efficient 3D Instance Mapping and Localization with Neural Fields
The first phase, InstanceMap, takes as input 2D segmentation masks of the image sequence generated by a frontend instance segmentation model, and associates corresponding masks across images to 3D labels.
