6 research outputs found
Tackling 3D ToF Artifacts Through Learning and the FLAT Dataset
Scene motion, multiple reflections, and sensor noise introduce artifacts in
the depth reconstruction performed by time-of-flight cameras. We propose a
two-stage, deep-learning approach to address all of these sources of artifacts
simultaneously. We also introduce FLAT, a synthetic dataset of 2000 ToF
measurements that capture all of these nonidealities, and allows to simulate
different camera hardware. Using the Kinect 2 camera as a baseline, we show
improved reconstruction errors over state-of-the-art methods, on both simulated
and real data.Comment: ECCV 201
Uncertainty-Aware CNNs for Depth Completion: Uncertainty from Beginning to End
The focus in deep learning research has been mostly to push the limits of
prediction accuracy. However, this was often achieved at the cost of increased
complexity, raising concerns about the interpretability and the reliability of
deep networks. Recently, an increasing attention has been given to untangling
the complexity of deep networks and quantifying their uncertainty for different
computer vision tasks. Differently, the task of depth completion has not
received enough attention despite the inherent noisy nature of depth sensors.
In this work, we thus focus on modeling the uncertainty of depth data in depth
completion starting from the sparse noisy input all the way to the final
prediction.
We propose a novel approach to identify disturbed measurements in the input
by learning an input confidence estimator in a self-supervised manner based on
the normalized convolutional neural networks (NCNNs). Further, we propose a
probabilistic version of NCNNs that produces a statistically meaningful
uncertainty measure for the final prediction. When we evaluate our approach on
the KITTI dataset for depth completion, we outperform all the existing Bayesian
Deep Learning approaches in terms of prediction accuracy, quality of the
uncertainty measure, and the computational efficiency. Moreover, our small
network with 670k parameters performs on-par with conventional approaches with
millions of parameters. These results give strong evidence that separating the
network into parallel uncertainty and prediction streams leads to
state-of-the-art performance with accurate uncertainty estimates.Comment: CVPR2020 (8 pages + supplementary
Efficient Methods for Computational Light Transport
En esta tesis presentamos contribuciones sobre distintos retos computacionales relacionados con transporte de luz. Los algoritmos que utilizan información sobre el transporte de luz están presentes en muchas aplicaciones de hoy en dÃa, desde la generación de efectos visuales, a la detección de objetos en tiempo real. La luz es una valiosa fuente de información que nos permite entender y representar nuestro entorno, pero obtener y procesar esta información presenta muchos desafÃos debido a la complejidad de las interacciones entre la luz y la materia. Esta tesis aporta contribuciones en este tema desde dos puntos de vista diferentes: algoritmos en estado estacionario, en los que se asume que la velocidad de la luz es infinita; y algoritmos en estado transitorio, que tratan la luz no solo en el dominio espacial, sino también en el temporal. Nuestras contribuciones en algoritmos estacionarios abordan problemas tanto en renderizado offline como en tiempo real. Nos enfocamos en la reducción de varianza para métodos offline,proponiendo un nuevo método para renderizado eficiente de medios participativos. En renderizado en tiempo real, abordamos las limitacionesde consumo de baterÃa en dispositivos móviles proponiendo un sistema de renderizado que incrementa la eficiencia energética en aplicaciones gráficas en tiempo real. En el transporte de luz transitorio, formalizamos la simulación de este tipo transporte en este nuevo dominio, y presentamos nuevos algoritmos y métodos para muestreo eficiente para render transitorio. Finalmente, demostramos la utilidad de generar datos en este dominio, presentando un nuevo método para corregir interferencia multi-caminos en camaras Timeof- Flight, un problema patológico en el procesamiento de imágenes transitorias.n this thesis we present contributions to different challenges of computational light transport. Light transport algorithms are present in many modern applications, from image generation for visual effects to real-time object detection. Light is a rich source of information that allows us to understand and represent our surroundings, but obtaining and processing this information presents many challenges due to its complex interactions with matter. This thesis provides advances in this subject from two different perspectives: steady-state algorithms, where the speed of light is assumed infinite, and transient-state algorithms, which deal with light as it travels not only through space but also time. Our steady-state contributions address problems in both offline and real-time rendering. We target variance reduction in offline rendering by proposing a new efficient method for participating media rendering. In real-time rendering, we target energy constraints of mobile devices by proposing a power-efficient rendering framework for real-time graphics applications. In transient-state we first formalize light transport simulation under this domain, and present new efficient sampling methods and algorithms for transient rendering. We finally demonstrate the potential of simulated data to correct multipath interference in Time-of-Flight cameras, one of the pathological problems in transient imaging.<br /