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Found 45 papers, 33 papers with code
UniK3D: Universal Camera Monocular 3D Estimation
Monocular 3D estimation is crucial for visual perception.
Ranked #2 on
Monocular Depth Estimation
on KITTI Eigen split
UniDepthV2: Universal Monocular Metric Depth Estimation Made Simpler
However, the remarkable accuracy of recent MMDE methods is confined to their training domains.
Ranked #2 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
To address this limitation, we present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination.
Fine-Grained Spatial and Verbal Losses for 3D Visual Grounding
In addition, we equip the verbo-visual fusion module of our new 3D visual grounding architecture AsphaltNet with a top-down bidirectional attentive fusion block, which enables the supervisory signals from our two losses to propagate to the respective converse branches of the network and thus aid the latter to learn context-aware instance embeddings and grounding-aware verbal embeddings.
CAFuser: Condition-Aware Multimodal Fusion for Robust Semantic Perception of Driving Scenes
By contrast, we propose a novel, condition-aware multimodal fusion approach for robust semantic perception of driving scenes.
Ranked #1 on
Semantic Segmentation
on DeLiVER
The BRAVO Semantic Segmentation Challenge Results in UNCV2024
We propose the unified BRAVO challenge to benchmark the reliability of semantic segmentation models under realistic perturbations and unknown out-of-distribution (OOD) scenarios.
Bayesian Self-Training for Semi-Supervised 3D Segmentation
By constructing a heuristic $n$-partite matching algorithm, we extend the method to semi-supervised 3D instance segmentation, and finally, with the same building blocks, to dense 3D visual grounding.
Physically Feasible Semantic Segmentation
State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion, minimizing solely per-pixel or per-segment classification objectives on their training data.
Sun Off, Lights On: Photorealistic Monocular Nighttime Simulation for Robust Semantic Perception
Nighttime scenes are hard to semantically perceive with learned models and annotate for humans.
TrafficBots V1.5: Traffic Simulation via Conditional VAEs and Transformers with Relative Pose Encoding
In this technical report we present TrafficBots V1. 5, a baseline method for the closed-loop simulation of traffic agents.
Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar
Neural fields have been broadly investigated as scene representations for the reproduction and novel generation of diverse outdoor scenes, including those autonomous vehicles and robots must handle.
UniDepth: Universal Monocular Metric Depth Estimation
However, the remarkable accuracy of recent MMDE methods is confined to their training domains.
Ranked #7 on
Monocular Depth Estimation
on KITTI Eigen split
(using extra training data)
Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes
The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes.
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse conditions.
MuRF: Multi-Baseline Radiance Fields
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views).
Real-Time Motion Prediction via Heterogeneous Polyline Transformer with Relative Pose Encoding
The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants.
Four Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding
3D visual grounding is the task of localizing the object in a 3D scene which is referred by a description in natural language.
Condition-Invariant Semantic Segmentation
Motivated by these findings, we propose to leverage stylization in performing feature-level adaptation by aligning the internal network features extracted by the encoder of the network from the original and the stylized view of each input image with a novel feature invariance loss.
Maskomaly:Zero-Shot Mask Anomaly Segmentation
We present a simple and practical framework for anomaly segmentation called Maskomaly.
Advances in Deep Concealed Scene Understanding
Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive objects exhibiting camouflage.
iDisc: Internal Discretization for Monocular Depth Estimation
Our method sets the new state of the art with significant improvements on NYU-Depth v2 and KITTI, outperforming all published methods on the official KITTI benchmark.
Ranked #3 on
Surface Normals Estimation
on NYU Depth v2
CamDiff: Camouflage Image Augmentation via Diffusion Model
Specifically, we leverage the latent diffusion model to synthesize salient objects in camouflaged scenes, while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training (CLIP) model to prevent synthesis failures and ensure the synthesized object aligns with the input prompt.
OVeNet: Offset Vector Network for Semantic Segmentation
Motivated by this prior, we design a novel two-head network, named Offset Vector Network (OVeNet), which generates both standard semantic predictions and a dense 2D offset vector field indicating the offset from each pixel to the respective seed pixel, which is used to compute an alternative, seed-based semantic prediction.
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
Lidar is a vital sensor for estimating the depth of a scene.
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic Segmentation
We investigate normal-to-adverse condition model adaptation for semantic segmentation, whereby image-level correspondences are available in the target domain.
Ranked #1 on
Source-Free Domain Adaptation
on Cityscapes to ACDC
A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild
We instead propose a unified framework for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos.
Masked Vision-Language Transformer in Fashion
We present a masked vision-language transformer (MVLT) for fashion-specific multi-modal representation.
TT-NF: Tensor Train Neural Fields
Learning neural fields has been an active topic in deep learning research, focusing, among other issues, on finding more compact and easy-to-fit representations.
Refign: Align and Refine for Adaptation of Semantic Segmentation to Adverse Conditions
Due to the scarcity of dense pixel-level semantic annotations for images recorded in adverse visual conditions, there has been a keen interest in unsupervised domain adaptation (UDA) for the semantic segmentation of such images.
Ranked #1 on
Semantic Segmentation
on Dark Zurich
L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras
As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research.
HRFuser: A Multi-resolution Sensor Fusion Architecture for 2D Object Detection
Besides standard cameras, autonomous vehicles typically include multiple additional sensors, such as lidars and radars, which help acquire richer information for perceiving the content of the driving scene.
Ranked #1 on
2D Object Detection
on Clear Weather
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
We focus on the supervised setup, in which ground-truth depth is available only at training time.
Ranked #6 on
Depth Estimation
on NYU-Depth V2
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
Event-Based Fusion for Motion Deblurring with Cross-modal Attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times.
Ranked #2 on
Object Detection
on DSEC
Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather
2) Through extensive experiments with several state-of-the-art detection approaches, we show that our fog simulation can be leveraged to significantly improve the performance for 3D object detection in the presence of fog.
Ranked #1 on
3D Object Detection
on Dense Fog
ACDC: The Adverse Conditions Dataset with Correspondences for Robust Semantic Driving Scene Perception
Level-5 driving automation requires a robust visual perception system that can parse input images under any condition.
Map-Guided Curriculum Domain Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night through progressively darker times of day, exploiting cross-time-of-day correspondences between daytime images from a reference map and dark images to guide the label inference in the dark domains; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, comprising 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 201 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark for our novel evaluation.
Ranked #5 on
Semantic Segmentation
on Nighttime Driving
Semantic Understanding of Foggy Scenes with Purely Synthetic Data
This work addresses the problem of semantic scene understanding under foggy road conditions.
Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation.
Ranked #7 on
Semantic Segmentation
on Nighttime Driving
Curriculum Model Adaptation with Synthetic and Real Data for Semantic Foggy Scene Understanding
The method is based on the fact that the results of semantic segmentation in moderately adverse conditions (light fog) can be bootstrapped to solve the same problem in highly adverse conditions (dense fog).
Ranked #5 on
Domain Adaptation
on Cityscapes-to-FoggyDriving
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding
In addition, we present three other main stand-alone contributions: 1) a novel method to add synthetic fog to real, clear-weather scenes using semantic input; 2) a new fog density estimator; 3) the Foggy Zurich dataset comprising $3808$ real foggy images, with pixel-level semantic annotations for $16$ images with dense fog.
Domain Adaptive Faster R-CNN for Object Detection in the Wild
The results demonstrate the effectiveness of our proposed approach for robust object detection in various domain shift scenarios.
Semantic Foggy Scene Understanding with Synthetic Data
Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN).
Theoretical Analysis of Active Contours on Graphs
Active contour models based on partial differential equations have proved successful in image segmentation, yet the study of their geometric formulation on arbitrary geometric graphs is still at an early stage.
