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Found 14 papers, 9 papers with code
Locate 3D: Real-World Object Localization via Self-Supervised Learning in 3D
LOCATE 3D sets a new state-of-the-art on standard referential grounding benchmarks and showcases robust generalization capabilities.
Unifying 2D and 3D Vision-Language Understanding
With these innovations, our model achieves state-of-the-art performance across multiple 3D vision-language grounding tasks, demonstrating the potential of transferring advances from 2D vision-language learning to the data-constrained 3D domain.
LIFT-GS: Cross-Scene Render-Supervised Distillation for 3D Language Grounding
Our approach to training 3D vision-language understanding models is to train a feedforward model that makes predictions in 3D, but never requires 3D labels and is supervised only in 2D, using 2D losses and differentiable rendering.
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
Multi-view 3D reconstruction remains a core challenge in computer vision, particularly in applications requiring accurate and scalable representations across diverse perspectives.
OpenEQA: Embodied Question Answering in the Era of Foundation Models
We present a modern formulation of Embodied Question Answering (EQA) as the task of understanding an environment well enough to answer questions about it in natural language.
Omnidata: A Scalable Pipeline for Making Multi-Task Mid-Level Vision Datasets from 3D Scans
This paper introduces a pipeline to parametrically sample and render multi-task vision datasets from comprehensive 3D scans from the real world.
Robustness via Cross-Domain Ensembles
We present a method for making neural network predictions robust to shifts from the training data distribution.
Robust Policies via Mid-Level Visual Representations: An Experimental Study in Manipulation and Navigation
Vision-based robotics often separates the control loop into one module for perception and a separate module for control.
Robust Learning Through Cross-Task Consistency
Visual perception entails solving a wide set of tasks, e. g., object detection, depth estimation, etc.
Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks
When training a neural network for a desired task, one may prefer to adapt a pre-trained network rather than starting from randomly initialized weights.
Learning to Navigate Using Mid-Level Visual Priors
How much does having visual priors about the world (e. g. the fact that the world is 3D) assist in learning to perform downstream motor tasks (e. g. navigating a complex environment)?
Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Visuomotor Policies
This skill set (hereafter mid-level perception) provides the policy with a more processed state of the world compared to raw images.
Gibson Env: Real-World Perception for Embodied Agents
Developing visual perception models for active agents and sensorimotor control are cumbersome to be done in the physical world, as existing algorithms are too slow to efficiently learn in real-time and robots are fragile and costly.
Taskonomy: Disentangling Task Transfer Learning
The product is a computational taxonomic map for task transfer learning.
