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Kevis-Kokitsi Maninis

Kevis-Kokitsi Maninis

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    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
    Vid2CAD: CAD Model Alignment using Multi-View Constraints from Videos
    Matthias Niessner
    Vittorio Ferrari
    Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2022)
    Preview abstract We address the task of aligning CAD models to a video sequence of a complex scene containing multiple objects. Our method can process arbitrary videos and fully automatically recover the 9 DoF pose for each object appearing in it, thus aligning them in a common 3D coordinate frame. The core idea of our method is to integrate neural network predictions from individual frames with a temporally global, multi-view constraint optimization formulation. This integration process resolves the scale and depth ambiguities in the per-frame predictions, and generally improves the estimate of all pose parameters. By leveraging multi-view constraints, our method also resolves occlusions and handles objects that are out of view in individual frames, thus reconstructing all objects into a single globally consistent CAD representation of the scene. In comparison to the state-of-the-art single-frame method Mask2CAD that we build on, we achieve substantial improvements on the Scan2CAD dataset (from 11.6% to 30.7% class average accuracy). View details
    Preview abstract We propose a transformer-based neural network architecture for multi-object 3D reconstruction from RGB videos. It relies on two alternative ways to represent its knowledge: as a global 3D grid of features and an array of view-specific 2D grids. We progressively exchange information between the two with a dedicated bidirectional attention mechanism. We exploit knowledge about the image formation process to significantly sparsify the attention weight matrix, making our architecture feasible on current hardware, both in terms of memory and computation. We attach a DETR-style head [9] on top of the 3D feature grid in order to detect the objects in the scene and to predict their 3D pose and 3D shape. Compared to previous methods, our architecture is single stage, end-to-end trainable, and it can reason holistically about a scene from multiple video frames without needing a brittle tracking step. We evaluate our method on the challenging Scan2CAD dataset [3], where we outperform (1) recent state-of-the-art methods [38,33] for 3D object pose estimation from RGB videos; and (2) a strong alternative method combining Multi-view Stereo [16] with RGB-D CAD alignment [4]. We plan to release our source code. View details
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