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Found 17 papers, 9 papers with code
Incorporating dense metric depth into neural 3D representations for view synthesis and relighting
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-manipulation, autonomous driving, convenient product capture, and consumer-level photography.
DataComp-LM: In search of the next generation of training sets for language models
We introduce DataComp for Language Models (DCLM), a testbed for controlled dataset experiments with the goal of improving language models.
Self-Supervised Geometry-Guided Initialization for Robust Monocular Visual Odometry
Monocular visual odometry is a key technology in a wide variety of autonomous systems.
Linearizing Large Language Models
Linear transformers have emerged as a subquadratic-time alternative to softmax attention and have garnered significant interest due to their fixed-size recurrent state that lowers inference cost.
Transcrib3D: 3D Referring Expression Resolution through Large Language Models
We introduce Transcrib3D, an approach that brings together 3D detection methods and the emergent reasoning capabilities of large language models (LLMs).
Language models scale reliably with over-training and on downstream tasks
Second, we relate the perplexity of a language model to its downstream task performance by proposing a power law.
Robust Self-Supervised Extrinsic Self-Calibration
Autonomous vehicles and robots need to operate over a wide variety of scenarios in order to complete tasks efficiently and safely.
Towards Zero-Shot Scale-Aware Monocular Depth Estimation
Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions.
NeRFuser: Large-Scale Scene Representation by NeRF Fusion
A practical benefit of implicit visual representations like Neural Radiance Fields (NeRFs) is their memory efficiency: large scenes can be efficiently stored and shared as small neural nets instead of collections of images.
DeLiRa: Self-Supervised Depth, Light, and Radiance Fields
In this work, we propose to use the multi-view photometric objective from the self-supervised depth estimation literature as a geometric regularizer for volumetric rendering, significantly improving novel view synthesis without requiring additional information.
Neural Camera Models
Modern computer vision has moved beyond the domain of internet photo collections and into the physical world, guiding camera-equipped robots and autonomous cars through unstructured environments.
Depth Field Networks for Generalizable Multi-view Scene Representation
Modern 3D computer vision leverages learning to boost geometric reasoning, mapping image data to classical structures such as cost volumes or epipolar constraints to improve matching.
Self-Supervised Camera Self-Calibration from Video
Camera calibration is integral to robotics and computer vision algorithms that seek to infer geometric properties of the scene from visual input streams.
Full Surround Monodepth from Multiple Cameras
In this work, we extend monocular self-supervised depth and ego-motion estimation to large-baseline multi-camera rigs.
Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
Self-supervised learning has emerged as a powerful tool for depth and ego-motion estimation, leading to state-of-the-art results on benchmark datasets.
DIODE: A Dense Indoor and Outdoor DEpth Dataset
We introduce DIODE, a dataset that contains thousands of diverse high resolution color images with accurate, dense, long-range depth measurements.
Examining the Impact of Blur on Recognition by Convolutional Networks
We investigate the extent to which this degradation is due to the mismatch between training and input image statistics.
