LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow

TL;DR

LayeredFlow is a comprehensive real-world benchmark with multi-layer annotations for non-Lambertian objects, facilitating the development and evaluation of multi-layer optical flow algorithms in diverse scenes.

Contribution

The paper introduces LayeredFlow, a new large-scale benchmark with multi-layer ground truth for non-Lambertian objects, and a synthetic dataset for training multi-layer optical flow models.

Findings

Training on synthetic data improves multi-layer optical flow prediction.

Fine-tuning on the dataset boosts performance on non-Lambertian objects.

Benchmark exhibits high scene and object diversity.

Abstract

Achieving 3D understanding of non-Lambertian objects is an important task with many useful applications, but most existing algorithms struggle to deal with such objects. One major obstacle towards progress in this field is the lack of holistic non-Lambertian benchmarks -- most benchmarks have low scene and object diversity, and none provide multi-layer 3D annotations for objects occluded by transparent surfaces. In this paper, we introduce LayeredFlow, a real world benchmark containing multi-layer ground truth annotation for optical flow of non-Lambertian objects. Compared to previous benchmarks, our benchmark exhibits greater scene and object diversity, with 150k high quality optical flow and stereo pairs taken over 185 indoor and outdoor scenes and 360 unique objects. Using LayeredFlow as evaluation data, we propose a new task called multi-layer optical flow. To provide training data for this task, we introduce a large-scale densely-annotated synthetic dataset containing 60k images within 30 scenes tailored for non-Lambertian objects. Training on our synthetic dataset enables model to predict multi-layer optical flow, while fine-tuning existing optical flow methods on the dataset notably boosts their performance on non-Lambertian objects without compromising the performance on diffuse objects. Data is available at https://layeredflow.cs.princeton.edu.