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Found 17 papers, 7 papers with code
Inverse Neural Rendering for Explainable Multi-Object Tracking
We propose to recast 3D multi-object tracking from RGB cameras as an \emph{Inverse Rendering (IR)} problem, by optimizing via a differentiable rendering pipeline over the latent space of pre-trained 3D object representations and retrieve the latents that best represent object instances in a given input image.
Thin On-Sensor Nanophotonic Array Cameras
The optical array is embedded on a metasurface that, at 700~nm height, is flat and sits on the sensor cover glass at 2. 5~mm focal distance from the sensor.
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.
ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering
With data as bottleneck and most of today's training data relying on good weather conditions with inclement weather as outlier, we rely on an inverse rendering approach to reconstruct the scene content.
LiDAR-in-the-Loop Hyperparameter Optimization
The resulting point clouds output by these DSPs are input to downstream 3D vision models -- both, in the form of training datasets or as input at inference time.
Simulating Road Spray Effects in Automotive Lidar Sensor Models
The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume.
LiDAR Snowfall Simulation for Robust 3D Object Detection
Due to the difficulty of collecting and annotating training data in this setting, we propose a physically based method to simulate the effect of snowfall on real clear-weather LiDAR point clouds.
Ranked #1 on
3D Object Detection
on Heavy Snowfall
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.
A Benchmark for Spray from Nearby Cutting Vehicles
Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas.
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media
Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets.
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.
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Autonomous driving at level five does not only means self-driving in the sunshine.
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions.
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available.
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
Gated2Depth: Real-time Dense Lidar from Gated Images
The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges.
Suppression of topological Mott-Hubbard phases by multiple charge orders in the honeycomb extended Hubbard model
We investigate the competition between charge-density-wave (CDW) states and a Coulomb interaction-driven topological Mott insulator (TMI) in the honeycomb extended Hubbard model.
Strongly Correlated Electrons
