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Found 19 papers, 9 papers with code
INQUIRE: A Natural World Text-to-Image Retrieval Benchmark
Detailed evaluation of a range of recent multimodal models demonstrates that INQUIRE poses a significant challenge, with the best models failing to achieve an mAP@50 above 50%.
Deep learning-based ecological analysis of camera trap images is impacted by training data quality and size
We assess the impact of model architecture, training data noise, and dataset size on ecological metrics, including species richness, occupancy, and activity patterns.
GroundUp: Rapid Sketch-Based 3D City Massing
Inspired by feedback from architects and existing workflows, our system takes as a first input a user sketch of multiple buildings in a top-down view.
TAPVid-3D: A Benchmark for Tracking Any Point in 3D
We introduce a new benchmark, TAPVid-3D, for evaluating the task of long-range Tracking Any Point in 3D (TAP-3D).
DoubleTake: Geometry Guided Depth Estimation
Estimating depth from a sequence of posed RGB images is a fundamental computer vision task, with applications in augmented reality, path planning etc.
AirPlanes: Accurate Plane Estimation via 3D-Consistent Embeddings
We show through extensive evaluation on the ScanNetV2 dataset that our new method outperforms existing approaches and our strong geometric baseline for the task of plane estimation.
Removing Objects From Neural Radiance Fields
We validate our approach using a new and still-challenging dataset for the task of NeRF inpainting.
Interactive Sketching of Mannequin Poses
It can be easy and even fun to sketch humans in different poses.
SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models
To combat this, a series of light-weight adaptation methods have been proposed to efficiently adapt such models when limited supervision is available.
Focus on the Positives: Self-Supervised Learning for Biodiversity Monitoring
We address the problem of learning self-supervised representations from unlabeled image collections.
The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth
We propose ManyDepth, an adaptive approach to dense depth estimation that can make use of sequence information at test time, when it is available.
Monocular Depth Estimation
Unsupervised Monocular Depth Estimation
Panoptic Segmentation Forecasting
Following this decomposition, we introduce panoptic segmentation forecasting.
Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision
Visual re-localization means using a single image as input to estimate the camera's location and orientation relative to a pre-recorded environment.
LookOut! Interactive Camera Gimbal Controller for Filming Long Takes
The job of a camera operator is challenging, and potentially dangerous, when filming long moving camera shots.
Graphics Human-Computer Interaction Robotics
Improved Handling of Motion Blur in Online Object Detection
Also, in contrast to findings from classification, we see a noteworthy boost by conditioning our model on bespoke categories of motion blur.
Single-Image Depth Prediction Makes Feature Matching Easier
Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines.
Deep Blending for Free-Viewpoint Image-Based-Rendering
We present a new deep learning approach to blending for IBR, in which we use held-out real image data to learn blending weights to combine input photo contributions.
Digging Into Self-Supervised Monocular Depth Estimation
Per-pixel ground-truth depth data is challenging to acquire at scale.
Ranked #2 on
Monocular Depth Estimation
on Make3D
CubeNet: Equivariance to 3D Rotation and Translation
3D Convolutional Neural Networks are sensitive to transformations applied to their input.
