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Diffusion for Out-of-Distribution Detection on Road Scenes and Beyond
Silvio Galesso*,
Philipp Schröppel*,
Hssan Driss,
Thomas Brox
(* denotes equal contribution)
ECCV, 2024
Paper / Code / ADE-OoD Benchmark / Bibtex
We introduce ADE-OoD, a novel benchmark for out-of-distribution (OoD) detection for semantic segmentation on general natural images, and DOoD, a diffusion-based method that detects OoD samples by perturbing them and checking the directional error of the estimated score.
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Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation
Philipp Schröppel,
Christopher Wewer,
Jan Eric Lenssen,
Eddy Ilg,
Thomas Brox
CVPR, 2024
Paper / Project page / Code / Bibtex
We generate 3D shape and appearance of objects via diffusion on neural point clouds (points with a 3D position and a learned feature). As the point positions represent the coarse shape and the features represent the appearance, shape and appearance can be generated separately.
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A Benchmark and a Baseline for Robust Multi-view Depth Estimation
Philipp Schröppel,
Jan Bechtold,
Artemij Amiranashvili,
Thomas Brox
3DV, 2022
Paper / Code / Bibtex
We introduce a benchmark for robust zero-shot multi-view depth estimation and evaluate many recent models. We present a new model that works more robustly across data from different domains and can serve as a baseline for future evaluations on the benchmark.
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SF2SE3: Clustering Scene Flow into SE (3)-Motions via Proposal and Selection
Leonhard Sommer,
Philipp Schröppel,
Thomas Brox
GCPR, 2022
Paper / Code / Bibtex
We propose SF2SE3, an approach estimate a moving object segmentation and corresponding SE(3) motions from optical flow and depth. It works by iteratively sampling motion proposals and selecting the best ones with respect to a maximum coverage formulation.
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Semi-Supervised Disparity Estimation with Deep Feature Reconstruction
Julia Guerrero-Viu,
Sergio Izquierdo,
Philipp Schröppel,
Thomas Brox
CVPR Women in Computer Vision Workshop, 2021
Paper
We train a network for disparity estimation in a semi-supervised fashion on labeled synthetic data and unlabeled real data. For supervision on the unlabeled data, we explore a deep feature reconstruction loss, instead of the commonly used photometric loss.
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