Search Results for author:
Found 44 papers, 29 papers with code
ADAPT: Action-aware Driving Caption Transformer
To bridge the gap, we propose an end-to-end transformer-based architecture, ADAPT (Action-aware Driving cAPtion Transformer), which provides user-friendly natural language narrations and reasoning for each decision making step of autonomous vehicular control and action.
SNAKE: Shape-aware Neural 3D Keypoint Field
Detecting 3D keypoints from point clouds is important for shape reconstruction, while this work investigates the dual question: can shape reconstruction benefit 3D keypoint detection?
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
By fitting a bridge-shaped curve to the illumination map distribution, both regions are suppressed and two tasks are bridged naturally.
LODE: Locally Conditioned Eikonal Implicit Scene Completion from Sparse LiDAR
In this paper, we propose a novel Eikonal formulation that conditions the implicit representation on localized shape priors which function as dense boundary value constraints, and demonstrate it works on SemanticKITTI and SemanticPOSS.
VIBUS: Data-efficient 3D Scene Parsing with VIewpoint Bottleneck and Uncertainty-Spectrum Modeling
In the first stage, we perform self-supervised representation learning on unlabeled points with the proposed Viewpoint Bottleneck loss function.
TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation
As such, we study the challenging problem of task oriented detection, which aims to find objects that best afford an action indicated by verbs like sit comfortably on.
Semi-supervised Implicit Scene Completion from Sparse LiDAR
Recent advances show that semi-supervised implicit representation learning can be achieved through physical constraints like Eikonal equations.
From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds
But adapting this scheme to the state-of-the-art (SOTA) solution for PC-based layout estimation is not straightforward.
Delving into Shape-aware Zero-shot Semantic Segmentation
Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy.
3D Implicit Transporter for Temporally Consistent Keypoint Discovery
To address this issue, the Transporter method was introduced for 2D data, which reconstructs the target frame from the source frame to incorporate both spatial and temporal information.
DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection
While this paradigm is natural for image-level or pixel-level prediction, adapting it to the detection problem is challenged by the issue of proposal matching.
PQ-Transformer: Jointly Parsing 3D Objects and Layouts from Point Clouds
Such a scheme has two limitations: 1) Storing and running several networks for different tasks are expensive for typical robotic platforms.
PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detection
Furthermore, we provide an open-source benchmark library, including dataset and baseline methods that cover 8 anomaly detection paradigms, to facilitate future research and application in this domain.
Ranked #2 on
Anomaly Detection
on PAD Dataset
STRAP: Structured Object Affordance Segmentation with Point Supervision
By label affinity, we refer to affordance segmentation as a multi-label prediction problem: A plate can be both holdable and containable.
Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing
Multi-task indoor scene understanding is widely considered as an intriguing formulation, as the affinity of different tasks may lead to improved performance.
Ranked #51 on
Semantic Segmentation
on NYU Depth v2
INT2: Interactive Trajectory Prediction at Intersections
One of the most challenging problems in motion forecasting is interactive trajectory prediction, whose goal is to jointly forecasts the future trajectories of interacting agents.
Pointly-supervised 3D Scene Parsing with Viewpoint Bottleneck
Semantic understanding of 3D point clouds is important for various robotics applications.
When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning
This brings up a new question: is it possible to combine learning from limited real data in offline RL and unrestricted exploration through imperfect simulators in online RL to address the drawbacks of both approaches?
LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF
In this paper, we present LATITUDE: Global Localization with Truncated Dynamic Low-pass Filter, which introduces a two-stage localization mechanism in city-scale NeRF.
Language-guided Semantic Style Transfer of 3D Indoor Scenes
We address the new problem of language-guided semantic style transfer of 3D indoor scenes.
Distance-Aware Occlusion Detection with Focused Attention
In this work, (1) we propose a novel three-decoder architecture as the infrastructure for focused attention; 2) we use the generalized intersection box prediction task to effectively guide our model to focus on occlusion-specific regions; 3) our model achieves a new state-of-the-art performance on distance-aware relationship detection.
Human-Object Interaction Detection
Relationship Detection
+1
DPF: Learning Dense Prediction Fields with Weak Supervision
We showcase the effectiveness of DPFs using two substantially different tasks: high-level semantic parsing and low-level intrinsic image decomposition.
Understanding Embodied Reference with Touch-Line Transformer
Human studies have revealed that objects referred to or pointed to do not lie on the elbow-wrist line, a common misconception; instead, they lie on the so-called virtual touch line.
Car-Studio: Learning Car Radiance Fields from Single-View and Endless In-the-wild Images
However, previous studies learned within a sequence of autonomous driving datasets, resulting in unsatisfactory blurring when rotating the car in the simulator.
NeRRF: 3D Reconstruction and View Synthesis for Transparent and Specular Objects with Neural Refractive-Reflective Fields
In this paper, we introduce the refractive-reflective field.
Planning Assembly Sequence with Graph Transformer
In this paper, we present a graph-transformer based framework for the ASP problem which is trained and demonstrated on a self-collected ASP database.
A Versatile and Efficient Reinforcement Learning Framework for Autonomous Driving
Heated debates continue over the best autonomous driving framework.
A Comprehensive Survey of Cross-Domain Policy Transfer for Embodied Agents
Consequently, researchers often resort to data from easily accessible source domains, such as simulation and laboratory environments, for cost-effective data acquisition and rapid model iteration.
ECT: Fine-grained Edge Detection with Learned Cause Tokens
To address these three issues, we propose a two-stage transformer-based network sequentially predicting generic edges and fine-grained edges, which has a global receptive field thanks to the attention mechanism.
Beyond SIFT using Binary features for Loop Closure Detection
The proposed system originates from our previous work Multi-Index hashing for Loop closure Detection (MILD), which employs Multi-Index Hashing (MIH)~\cite{greene1994multi} for Approximate Nearest Neighbor (ANN) search of binary features.
Utilizing High-level Visual Feature for Indoor Shopping Mall Navigation
Towards robust and convenient indoor shopping mall navigation, we propose a novel learning-based scheme to utilize the high-level visual information from the storefront images captured by personal devices of users.
FlyCap: Markerless Motion Capture Using Multiple Autonomous Flying Cameras
We propose a novel non-rigid surface registration method to track and fuse the depth of the three flying cameras for surface motion tracking of the moving target, and simultaneously calculate the pose of each flying camera.
Efficient Robotic Manipulation Through Offline-to-Online Reinforcement Learning and Goal-Aware State Information
End-to-end learning robotic manipulation with high data efficiency is one of the key challenges in robotics.
Discriminator-Guided Model-Based Offline Imitation Learning
In this paper, we propose the Discriminator-guided Model-based offline Imitation Learning (DMIL) framework, which introduces a discriminator to simultaneously distinguish the dynamics correctness and suboptimality of model rollout data against real expert demonstrations.
City-scale Incremental Neural Mapping with Three-layer Sampling and Panoptic Representation
Neural implicit representations are drawing a lot of attention from the robotics community recently, as they are expressive, continuous and compact.
A High Fidelity Simulation Framework for Potential Safety Benefits Estimation of Cooperative Pedestrian Perception
This paper proposes a high-fidelity simulation framework that can estimate the potential safety benefits of vehicle-to-infrastructure (V2I) pedestrian safety strategies.
Self-Aligning Depth-regularized Radiance Fields for Asynchronous RGB-D Sequences
It has been shown that learning radiance fields with depth rendering and depth supervision can effectively promote the quality and convergence of view synthesis.
Incorporating Recurrent Reinforcement Learning into Model Predictive Control for Adaptive Control in Autonomous Driving
Model Predictive Control (MPC) is attracting tremendous attention in the autonomous driving task as a powerful control technique.
H2O+: An Improved Framework for Hybrid Offline-and-Online RL with Dynamics Gaps
Solving real-world complex tasks using reinforcement learning (RL) without high-fidelity simulation environments or large amounts of offline data can be quite challenging.
ASSIST: Interactive Scene Nodes for Scalable and Realistic Indoor Simulation
We present ASSIST, an object-wise neural radiance field as a panoptic representation for compositional and realistic simulation.
Latency-aware Road Anomaly Segmentation in Videos: A Photorealistic Dataset and New Metrics
We believe the latter is valuable as it measures whether an anomaly segmentation algorithm can truly prevent a car from crashing in a temporally informed setting.
Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context.
More Than Routing: Joint GPS and Route Modeling for Refine Trajectory Representation Learning
The experimental results demonstrate that JGRM outperforms existing methods in both road segment representation and trajectory representation tasks.
PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments
Robotic manipulation of ungraspable objects with two-finger grippers presents significant challenges due to the paucity of graspable features, while traditional pre-grasping techniques, which rely on repositioning objects and leveraging external aids like table edges, lack the adaptability across object categories and scenes.
