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Found 18 papers, 3 papers with code
LightSim: Neural Lighting Simulation for Urban Scenes
Different outdoor illumination conditions drastically alter the appearance of urban scenes, and they can harm the performance of image-based robot perception systems if not seen during training.
Real-Time Neural Rasterization for Large Scenes
We propose a new method for realistic real-time novel-view synthesis (NVS) of large scenes.
Reconstructing Objects in-the-wild for Realistic Sensor Simulation
In this work, we present NeuSim, a novel approach that estimates accurate geometry and realistic appearance from sparse in-the-wild data captured at distance and at limited viewpoints.
CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
To tackle these issues, we present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry, including articulated wheels, with high-quality appearance.
Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
In this paper, we propose a framework, Adv3D, that takes real world scenarios and performs closed-loop sensor simulation to evaluate autonomy performance, and finds vehicle shapes that make the scenario more challenging, resulting in autonomy failures and uncomfortable SDV maneuvers.
UniSim: A Neural Closed-Loop Sensor Simulator
Previously recorded driving logs provide a rich resource to build these new scenarios from, but for closed loop evaluation, we need to modify the sensor data based on the new scene configuration and the SDV's decisions, as actors might be added or removed and the trajectories of existing actors and the SDV will differ from the original log.
Towards Zero Domain Gap: A Comprehensive Study of Realistic LiDAR Simulation for Autonomy Testing
We leverage this setting to analyze what aspects of LiDAR simulation, such as pulse phenomena, scanning effects, and asset quality, affect the domain gap with respect to the autonomy system, including perception, prediction, and motion planning, and analyze how modifications to the simulated LiDAR influence each part.
Deep Feedback Inverse Problem Solver
Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation.
Mending Neural Implicit Modeling for 3D Vehicle Reconstruction in the Wild
Reconstructing high-quality 3D objects from sparse, partial observations from a single view is of crucial importance for various applications in computer vision, robotics, and graphics.
S3: Neural Shape, Skeleton, and Skinning Fields for 3D Human Modeling
Constructing and animating humans is an important component for building virtual worlds in a wide variety of applications such as virtual reality or robotics testing in simulation.
Adversarial Attacks On Multi-Agent Communication
Growing at a fast pace, modern autonomous systems will soon be deployed at scale, opening up the possibility for cooperative multi-agent systems.
GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving.
AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
Importantly, by simulating directly from sensor data, we obtain adversarial scenarios that are safety-critical for the full autonomy stack.
SceneGen: Learning to Generate Realistic Traffic Scenes
Existing methods typically insert actors into the scene according to a set of hand-crafted heuristics and are limited in their ability to model the true complexity and diversity of real traffic scenes, thus inducing a content gap between synthesized traffic scenes versus real ones.
Weakly-supervised 3D Shape Completion in the Wild
3D shape completion for real data is important but challenging, since partial point clouds acquired by real-world sensors are usually sparse, noisy and unaligned.
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles.
Ranked #1 on
3D Object Detection
on OPV2V
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
We first utilize ray casting over the 3D scene and then use a deep neural network to produce deviations from the physics-based simulation, producing realistic LiDAR point clouds.
Zero-Shot Compositional Policy Learning via Language Grounding
To facilitate the research on language-guided agents with domain adaption, we propose a novel zero-shot compositional policy learning task, where the environments are characterized as a composition of different attributes.
