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Found 23 papers, 2 papers with code
Neural Radiance Fields with Torch Units
In this paper, we design a novel inference pattern that encourages a single camera ray possessing more contextual information, and models the relationship among sample points on each camera ray.
HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splatting
Holistic understanding of urban scenes based on RGB images is a challenging yet important problem.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images.
Learning Effective NeRFs and SDFs Representations with 3D Generative Adversarial Networks for 3D Object Generation: Technical Report for ICCV 2023 OmniObject3D Challenge
To resolve this problem, we study learning effective NeRFs and SDFs representations with 3D Generative Adversarial Networks (GANs) for 3D object generation.
MV-DeepSDF: Implicit Modeling with Multi-Sweep Point Clouds for 3D Vehicle Reconstruction in Autonomous Driving
This set-level latent code is an expression of the optimal 3D shape in the implicit space, and can be subsequently decoded to a continuous SDF of the vehicle.
Improving LiDAR 3D Object Detection via Range-based Point Cloud Density Optimization
In recent years, much progress has been made in LiDAR-based 3D object detection mainly due to advances in detector architecture designs and availability of large-scale LiDAR datasets.
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds
We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room.
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation
In this method, a dual-task 3D backbone is developed to extract both panoptic- and detection-level features from the input LiDAR point cloud.
NeRF-MS: Neural Radiance Fields with Multi-Sequence
Neural radiance fields (NeRF) achieve impressive performance in novel view synthesis when trained on only single sequence data.
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer
In this work, we propose the first attempt to explore multi-domain learning and generalization for LiDAR-based 3D object detection.
Domain Adaptation in 3D Object Detection with Gradual Batch Alternation Training
The idea is to initiate the training with the batch of samples from the source and target domain data in an alternate fashion, but then gradually reduce the amount of the source domain data over time as the training progresses.
PCGen: Point Cloud Generator for LiDAR Simulation
To tackle the first challenge, we propose FPA raycasting and surrogate model raydrop.
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation
In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone.
CPSeg: Cluster-free Panoptic Segmentation of 3D LiDAR Point Clouds
A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes.
How to Build a Curb Dataset with LiDAR Data for Autonomous Driving
Curbs are one of the essential elements of urban and highway traffic environments.
Unsupervised Domain Adaptation in LiDAR Semantic Segmentation with Self-Supervision and Gated Adapters
In this paper, we focus on a less explored, but more realistic and complex problem of domain adaptation in LiDAR semantic segmentation.
(AF)2-S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #3 on
3D Semantic Segmentation
on nuScenes
Bidirectional Attention Network for Monocular Depth Estimation
In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks.
TORNADO-Net: mulTiview tOtal vaRiatioN semAntic segmentation with Diamond inceptiOn module
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving.
Ranked #15 on
3D Semantic Segmentation
on SemanticKITTI
RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving
In this paper, we strive for solving the ambiguities arisen by the astoundingly high density of raw PseudoLiDAR for monocular 3D object detection for autonomous driving.
BoxNet: A Deep Learning Method for 2D Bounding Box Estimation from Bird's-Eye View Point Cloud
We present a learning-based method to estimate the object bounding box from its 2D bird's-eye view (BEV) LiDAR points.
Adaptive Hierarchical Down-Sampling for Point Cloud Classification
While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied.
