Search Results for author:
Found 14 papers, 7 papers with code
Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues
We propose Gated Stereo, a high-resolution and long-range depth estimation technique that operates on active gated stereo images.
S$^3$Track: Self-supervised Tracking with Soft Assignment Flow
With this training approach in hand, we develop an appearance-based model for learning instance-aware object features used to construct a cost matrix based on the pairwise distances between the object features.
The Differentiable Lens: Compound Lens Search over Glass Surfaces and Materials for Object Detection
Recently, joint optimization approaches that design lenses alongside other components of the image acquisition and processing pipeline -- notably, downstream neural networks -- have achieved improved imaging quality or better performance on vision tasks.
Gated2Gated: Self-Supervised Depth Estimation from Gated Images
Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain.
Neural Auto-Exposure for High-Dynamic Range Object Detection
In this work, we revisit auto-exposure control as an alternative to HDR sensors.
Adversarial Imaging Pipelines
As a result, optimized patterns can become adversarial for the classifier after being transformed by a certain camera ISP and optic but not for others.
Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues
Today's state-of-the-art methods for 3D object detection are based on lidar, stereo, or monocular cameras.
Neural Scene Graphs for Dynamic Scenes
Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images.
Pix2Shape: Towards Unsupervised Learning of 3D Scenes from Images using a View-based Representation
We propose Pix2Shape, an approach to solve this problem with four components: (i) an encoder that infers the latent 3D representation from an image, (ii) a decoder that generates an explicit 2. 5D surfel-based reconstruction of a scene from the latent code (iii) a differentiable renderer that synthesizes a 2D image from the surfel representation, and (iv) a critic network trained to discriminate between images generated by the decoder-renderer and those from a training distribution.
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
Pix2Scene: Learning Implicit 3D Representations from Images
Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e. g., simulation and robotics.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
We present a fully automatic system to optimize the parameters of black-box hardware and software image processing pipelines according to any arbitrary (i. e., application-specific) metric.
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Ranked #2 on
2D Object Detection
on Clear Weather
Steady-state Non-Line-of-Sight Imaging
Relying on consumer color image sensors, with high fill factor, high quantum efficiency and low read-out noise, we demonstrate high-fidelity color NLOS imaging for scene configurations tackled before with picosecond time resolution.
