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Found 28 papers, 16 papers with code
Q-SLAM: Quadric Representations for Monocular SLAM
This focus results in a significant disconnect between NeRF applications, i. e., novel-view synthesis and the requirements of SLAM.
StreamDiffusion: A Pipeline-level Solution for Real-time Interactive Generation
We introduce StreamDiffusion, a real-time diffusion pipeline designed for interactive image generation.
3DiffTection: 3D Object Detection with Geometry-Aware Diffusion Features
However, these features are initially trained on paired text and image data, which are not optimized for 3D tasks, and often exhibit a domain gap when applied to the target data.
What Matters to You? Towards Visual Representation Alignment for Robot Learning
When operating in service of people, robots need to optimize rewards aligned with end-user preferences.
Human-oriented Representation Learning for Robotic Manipulation
We introduce Task Fusion Decoder as a plug-and-play embedding translator that utilizes the underlying relationships among these perceptual skills to guide the representation learning towards encoding meaningful structure for what's important for all perceptual skills, ultimately empowering learning of downstream robotic manipulation tasks.
LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving
Existing learning-based autonomous driving (AD) systems face challenges in comprehending high-level information, generalizing to rare events, and providing interpretability.
RSRD: A Road Surface Reconstruction Dataset and Benchmark for Safe and Comfortable Autonomous Driving
This paper addresses the growing demands for safety and comfort in intelligent robot systems, particularly autonomous vehicles, where road conditions play a pivotal role in overall driving performance.
HallE-Control: Controlling Object Hallucination in Large Multimodal Models
Current Large Multimodal Models (LMMs) achieve remarkable progress, yet there remains significant uncertainty regarding their ability to accurately apprehend visual details, that is, in performing detailed captioning.
Pre-training on Synthetic Driving Data for Trajectory Prediction
We propose to augment both HD maps and trajectories and apply pre-training strategies on top of them.
DELFlow: Dense Efficient Learning of Scene Flow for Large-Scale Point Clouds
Previous methods rarely predict scene flow from the entire point clouds of the scene with one-time inference due to the memory inefficiency and heavy overhead from distance calculation and sorting involved in commonly used farthest point sampling, KNN, and ball query algorithms for local feature aggregation.
NeRF-Det: Learning Geometry-Aware Volumetric Representation for Multi-View 3D Object Detection
We present NeRF-Det, a novel method for indoor 3D detection with posed RGB images as input.
SparseFusion: Fusing Multi-Modal Sparse Representations for Multi-Sensor 3D Object Detection
However, given that objects occupy only a small part of a scene, finding dense candidates and generating dense representations is noisy and inefficient.
Quadric Representations for LiDAR Odometry, Mapping and Localization
Current LiDAR odometry, mapping and localization methods leverage point-wise representations of 3D scenes and achieve high accuracy in autonomous driving tasks.
Open-Vocabulary Point-Cloud Object Detection without 3D Annotation
In this paper, we address open-vocabulary 3D point-cloud detection by a dividing-and-conquering strategy, which involves: 1) developing a point-cloud detector that can learn a general representation for localizing various objects, and 2) connecting textual and point-cloud representations to enable the detector to classify novel object categories based on text prompting.
Time Will Tell: New Outlooks and A Baseline for Temporal Multi-View 3D Object Detection
While recent camera-only 3D detection methods leverage multiple timesteps, the limited history they use significantly hampers the extent to which temporal fusion can improve object perception.
Ranked #1 on
Robust Camera Only 3D Object Detection
on nuScenes-C
Open-Vocabulary 3D Detection via Image-level Class and Debiased Cross-modal Contrastive Learning
Current point-cloud detection methods have difficulty detecting the open-vocabulary objects in the real world, due to their limited generalization capability.
PreTraM: Self-Supervised Pre-training via Connecting Trajectory and Map
It is hard to replicate these approaches in trajectory forecasting due to the lack of adequate trajectory data (e. g., 34K samples in the nuScenes dataset).
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection
While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced by multi-modal fusion.
VisDrone-CC2020: The Vision Meets Drone Crowd Counting Challenge Results
Crowd counting on the drone platform is an interesting topic in computer vision, which brings new challenges such as small object inference, background clutter and wide viewpoint.
Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
We discover that we can indeed use the same architecture and pretrained weights of a neural net model to understand both images and point-clouds.
DCANet: Dense Context-Aware Network for Semantic Segmentation
As the superiority of context information gradually manifests in advanced semantic segmentation, learning to capture the compact context relationship can help to understand the complex scenes.
A Simple and Efficient Multi-task Network for 3D Object Detection and Road Understanding
Detecting dynamic objects and predicting static road information such as drivable areas and ground heights are crucial for safe autonomous driving.
Visual Transformers: Where Do Transformers Really Belong in Vision Models?
A recent trend in computer vision is to replace convolutions with transformers.
Sparse R-CNN: End-to-End Object Detection with Learnable Proposals
In our method, however, a fixed sparse set of learned object proposals, total length of $N$, are provided to object recognition head to perform classification and location.
Ranked #5 on
2D Object Detection
on CeyMo
Visual Transformers: Token-based Image Representation and Processing for Computer Vision
In this work, we challenge this paradigm by (a) representing images as semantic visual tokens and (b) running transformers to densely model token relationships.
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Using standard convolutions to process such LiDAR images is problematic, as convolution filters pick up local features that are only active in specific regions in the image.
Ranked #24 on
3D Semantic Segmentation
on SemanticKITTI
AutoScale: Learning to Scale for Crowd Counting and Localization
A major issue is that the density map on dense regions usually accumulates density values from a number of nearby Gaussian blobs, yielding different large density values on a small set of pixels.
Learn to Scale: Generating Multipolar Normalized Density Maps for Crowd Counting
Dense crowd counting aims to predict thousands of human instances from an image, by calculating integrals of a density map over image pixels.
