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Howard Zhou

Howard Zhou

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    LFM-3D: Learnable Feature Matching Across Wide Baselines Using 3D Signals
    Arjun Karpur
    Guilherme Perrotta
    Ricardo Martin-Brualla
    Proc. 3DV'24 (2024) (to appear)
    Preview abstract Finding localized correspondences across different images of the same object is crucial to understand its geometry. In recent years, this problem has seen remarkable progress with the advent of deep learning-based local image features and learnable matchers. Still, learnable matchers often underperform when there exists only small regions of co-visibility between image pairs (i.e. wide camera baselines). To address this problem, we leverage recent progress in coarse single-view geometry estimation methods. We propose LFM-3D, a Learnable Feature Matching framework that uses models based on graph neural networks and enhances their capabilities by integrating noisy, estimated 3D signals to boost correspondence estimation. When integrating 3D signals into the matcher model, we show that a suitable positional encoding is critical to effectively make use of the low-dimensional 3D information. We experiment with two different 3D signals - normalized object coordinates and monocular depth estimates - and evaluate our method on large-scale (synthetic and real) datasets containing object-centric image pairs across wide baselines. We observe strong feature matching improvements compared to 2D-only methods, with up to +6% total recall and +28% precision at fixed recall. Additionally, we demonstrate that the resulting improved correspondences lead to much higher relative posing accuracy for in-the-wild image pairs - up to 8.6% compared to the 2D-only approach. View details
    NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations
    Varun Jampani
    Andreas Engelhardt
    Arjun Karpur
    Karen Truong
    Kyle Sargent
    Ricardo Martin-Brualla
    Kaushal Patel
    Daniel Vlasic
    Vittorio Ferrari
    Ce Liu
    Neural Information Processing Systems (NeurIPS) (2023)
    Preview abstract Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where SfM techniques can provide ground-truth (GT) camera poses. We note that SfM techniques tend to fail on in-the-wild image collections such as image search results with varying backgrounds and illuminations. To enable systematic research progress on 3D reconstruction from casual image captures, we propose a new dataset of image collections called `NAVI' consisting of category-agnostic image collections of objects with high-quality 3D scans along with per-image 2D-3D alignments providing near-perfect GT camera parameters. These 2D-3D alignments allows to extract derivative annotations such as dense pixel correspondences, depth and segmentation maps. We demonstrate the use of NAVI image collections on different problem settings and show that NAVI enables more thorough evaluations that were not possible with existing datasets. We believe NAVI is beneficial for systematic research progress on 3D reconstruction and correspondence estimation. Project page: \url{https://navidataset.github.io} View details
    IBRNet: Learning Multi-View Image-Based Rendering
    Kyle Genova
    Pratul Srinivasan
    Qianqian Wang
    Ricardo Martin-Brualla
    Zhicheng Wang
    Conference on Computer Vision and Pattern Recognition (CVPR), IEEE (2021) (to appear)
    Preview abstract We present a method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views.The core of our method is a multilayer perceptron (MLP)that generates RGBA at each 5D coordinate from multi-view image features. Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that naturally generalizes to novel scene types and camera setups. Compared to previous generic image-based rendering (IBR) methods like Multiple-plane images (MPIs) that use discrete volume representations, our method instead produces RGBAs at continuous 5D locations (3D spatial locations and 2D viewing directions), enabling high-resolution imagery rendering.Our rendering pipeline is fully differentiable, and the only input required to train our method are multi-view posed images. Experiments show that our method outperforms previous IBR methods, and achieves state-of-the-art performance when fine tuned on each test scene. View details
    Preview abstract Most deep architectures for image classification – even those that are trained to classify a large number of diverse categories – learn shared image representations with a single combined model. Intuitively, however, categories that are more visually similar should share more information than those that are very different. While hierarchical deep networks address this problem by learning separate features for subsets of related categories, current implementations require simplified models using fixed architectures specified with heuristic clustering methods. Instead, we propose Blockout, a method for regularization and model selection that simultaneously learns both the model architecture and parameters jointly with end-to-end training. Inspired by dropout, our approach gives a novel parametrization of hierarchical architectures that allows for structure learning using simple back-propagation. To demonstrate the utility of our approach, we evaluate Blockout on the CIFAR and ImageNet datasets demonstrating improved classification accuracy, better regularization performance, faster training, and a clear separation of nodes into hierarchical structures. View details
    The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition
    Andrew Howard
    Alexander Toshev
    James Philbin
    Li Fei-Fei
    Computer Vision and Pattern Recognition (2016)
    Preview abstract Current approaches for fine-grained recognition do the following: First, recruit experts to annotate a dataset of images, optionally also collecting more structured data in the form of part annotations and bounding boxes. Second, train a model utilizing this data. Toward the goal of solving fine-grained recognition, we introduce an alternative approach, leveraging free, noisy data from the web and simple, generic methods of recognition. This approach has benefits in both performance and scalability. We demonstrate its efficacy on four fine-grained datasets, greatly exceeding existing state of the art without the manual collection of even a single label, and furthermore show first results at scaling to more than 10,000 fine-grained categories. Quantitatively, we achieve top-1 accuracies of 92.3% on CUB-200-2011, 85.4% on Birdsnap, 93.4% on FGVC-Aircraft, and 80.8% on Stanford Dogs without using their annotated training sets. We compare our approach to an active learning approach for expanding fine-grained datasets. View details
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