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Found 8 papers, 5 papers with code
Semantic Flow: Learning Semantic Field of Dynamic Scenes from Monocular Videos
As there is 2D-to-3D ambiguity problem in the viewing direction when extracting 3D flow features from 2D video frames, we consider the volume densities as opacity priors that describe the contributions of flow features to the semantics on the frames.
OASim: an Open and Adaptive Simulator based on Neural Rendering for Autonomous Driving
With the development of implicit rendering technology and in-depth research on using generative models to produce data at scale, we propose OASim, an open and adaptive simulator and autonomous driving data generator based on implicit neural rendering.
Towards Knowledge-driven Autonomous Driving
This paper explores the emerging knowledge-driven autonomous driving technologies.
ReSimAD: Zero-Shot 3D Domain Transfer for Autonomous Driving with Source Reconstruction and Target Simulation
Domain shifts such as sensor type changes and geographical situation variations are prevalent in Autonomous Driving (AD), which poses a challenge since AD model relying on the previous domain knowledge can be hardly directly deployed to a new domain without additional costs.
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
In this paper, we propose a radiologist-inspired method to simulate the diagnostic process of radiologists, which is composed of context parsing and prototype recalling modules.
StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views
We present a novel multi-view implicit surface reconstruction technique, termed StreetSurf, that is readily applicable to street view images in widely-used autonomous driving datasets, such as Waymo-perception sequences, without necessarily requiring LiDAR data.
ReVoLT: Relational Reasoning and Voronoi Local Graph Planning for Target-driven Navigation
However, most of them obtain robot actions directly from observations in an end-to-end manner via an incomplete relation graph, which is not interpretable and reliable.
Template NeRF: Towards Modeling Dense Shape Correspondences from Category-Specific Object Images
By representing object instances within the same category as shape and appearance variation of a shared NeRF template, our proposed method can achieve dense shape correspondences reasoning on images for a wide range of object classes.
