29 Result(s)
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Chapter and Conference Paper
Rethinking Closed-Loop Training for Autonomous Driving
Recent advances in high-fidelity simulators [22, 44, 82] have enabled closed-loop training of autonomous driving agents, potentially solving the distribution shift in training v.s. deployment and allowing trainin...
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Chapter and Conference Paper
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. Different from previous methods, we address th...
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Chapter and Conference Paper
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
We present a novel method for testing the safety of self-driving vehicles in simulation. We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps. Instead, we...
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Chapter and Conference Paper
Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
In this paper we propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles and produces interpretable intermediate representations. U...
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Chapter and Conference Paper
Learning Lane Graph Representations for Motion Forecasting
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, we construct a lane graph from...
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Chapter and Conference Paper
LevelSet R-CNN: A Deep Variational Method for Instance Segmentation
Obtaining precise instance segmentation masks is of high importance in many modern applications such as robotic manipulation and autonomous driving. Currently, many state of the art models are based on the Mas...
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Chapter and Conference Paper
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. By intelligently aggregating the information r...
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Chapter and Conference Paper
Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
In order to plan a safe maneuver an autonomous vehicle must accurately perceive its environment, and understand the interactions among traffic participants. In this paper, we aim to learn scene-consistent moti...
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Chapter and Conference Paper
Deep Feedback Inverse Problem Solver
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. S...
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Chapter and Conference Paper
RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects
We tackle the problem of exploiting Radar for perception in the context of self-driving as Radar provides complementary information to other sensors such as LiDAR or cameras in the form of Doppler velocity. Th...
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Chapter and Conference Paper
DSDNet: Deep Structured Self-driving Network
In this paper, we propose the Deep Structured self-Driving Network (DSDNet), which performs object detection, motion prediction, and motion planning with a single neural network. Towards this goal, we develop ...
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Chapter and Conference Paper
Dense RepPoints: Representing Visual Objects with Dense Point Sets
We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level. Techniques are proposed t...
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Chapter and Conference Paper
Conditional Entropy Coding for Efficient Video Compression
We propose a very simple and efficient video compression framework that only focuses on modeling the conditional entropy between frames. Unlike prior learning-based approaches, we reduce complexity by not perf...
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Chapter and Conference Paper
Single Image Intrinsic Decomposition Without a Single Intrinsic Image
Intrinsic image decomposition—decomposing a natural image into a set of images corresponding to different physical causes—is one of the key and fundamental problems of computer vision. Previous intrinsic decom...
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Chapter and Conference Paper
Deep Continuous Fusion for Multi-sensor 3D Object Detection
In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that...
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Chapter and Conference Paper
End-to-End Deep Structured Models for Drawing Crosswalks
In this paper we address the problem of detecting crosswalks from LiDAR and camera imagery. Towards this goal, given multiple LiDAR sweeps and the corresponding imagery, we project both inputs onto the ground ...
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Chapter and Conference Paper
Exploiting Semantic Information and Deep Matching for Optical Flow
We tackle the problem of estimating optical flow from a monocular camera in the context of autonomous driving. We build on the observation that the scene is typically composed of a static background, as well a...
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Chapter and Conference Paper
HouseCraft: Building Houses from Rental Ads and Street Views
In this paper, we utilize rental ads to create realistic textured 3D models of building exteriors. In particular, we exploit the address of the property and its floorplan, which are typically available in the ...
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Chapter and Conference Paper
A High Performance CRF Model for Clothes Parsing
In this paper we tackle the problem of clothing parsing: Our goal is to segment and classify different garments a person is wearing. We frame the problem as the one of inference in a pose-aware Conditional Ran...
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Chapter and Conference Paper
Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation
In this paper we propose a slanted plane model for jointly recovering an image segmentation, a dense depth estimate as well as boundary labels (such as occlusion boundaries) from a static scene given two frame...
