Search Results for author:
Found 21 papers, 10 papers with code
Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection
To tackle the challenges, we first collect a new multi-view LiDAR-camera 3D multiple-object-tracking benchmark of highly crowded pedestrians for in-depth analysis.
Ranked #1 on
3D Pedestrian Tracking
on nuScenes validation set
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
In this work, we propose a new robustness benchmark to evaluate the depth estimation system under various noisy pose settings.
Ranked #1 on
Monocular Depth Estimation
on DDAD
GIM: Learning Generalizable Image Matcher From Internet Videos
Given an architecture, GIM first trains it on standard domain-specific datasets and then combines it with complementary matching methods to create dense labels on nearby frames of novel videos.
Ranked #1 on
Image Matching
on ZEB
UC-NeRF: Neural Radiance Field for Under-Calibrated Multi-view Cameras in Autonomous Driving
Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities.
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image
State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a single camera model and are unable to perform mixed-data training due to the metric ambiguity.
Ranked #29 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Learning to Fuse Monocular and Multi-view Cues for Multi-frame Depth Estimation in Dynamic Scenes
To let the geometric perception learned from multi-view cues in static areas propagate to the monocular representation in dynamic areas and let monocular cues enhance the representation of multi-view cost volume, we propose a cross-cue fusion (CCF) module, which includes the cross-cue attention (CCA) to encode the spatially non-local relative intra-relations from each source to enhance the representation of the other.
The Second Monocular Depth Estimation Challenge
This paper discusses the results for the second edition of the Monocular Depth Estimation Challenge (MDEC).
Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion
In this paper, we propose a quality evaluation network to score the point clouds and help judge the quality of the point cloud before applying the completion model.
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning
The image-based 3D object detection task expects that the predicted 3D bounding box has a ``tightness'' projection (also referred to as cuboid), which fits the object contour well on the image while still keeping the geometric attribute on the 3D space, e. g., physical dimension, pairwise orthogonal, etc.
Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimation
During the iterative update, the results of depth estimation are compared across cameras and the information of overlapping areas is propagated to the whole depth maps with the help of basis formulation.
UniFusion: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird's-Eye-View
Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion.
MonoJSG: Joint Semantic and Geometric Cost Volume for Monocular 3D Object Detection
Based on the object depth, the dense coordinates patch together with the corresponding object features is reprojected to the image space to build a cost volume in a joint semantic and geometric error manner.
Temporal Point Cloud Completion with Pose Disturbance
With the help of gated recovery units(GRU) and attention mechanisms as temporal units, we propose a point cloud completion framework that accepts a sequence of unaligned and sparse inputs, and outputs consistent and aligned point clouds.
Geometry-based Distance Decomposition for Monocular 3D Object Detection
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Birds Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics.
Ranked #16 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Our method, called Stereo R-CNN, extends Faster R-CNN for stereo inputs to simultaneously detect and associate object in left and right images.
3D Object Detection From Stereo Images
Autonomous Driving
+3
Multi-View 3D Object Detection Network for Autonomous Driving
We encode the sparse 3D point cloud with a compact multi-view representation.
Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection
In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection.
3D Object Proposals using Stereo Imagery for Accurate Object Class Detection
We then exploit a CNN on top of these proposals to perform object detection.
Monocular 3D Object Detection for Autonomous Driving
The focus of this paper is on proposal generation.
Ranked #8 on
Vehicle Pose Estimation
on KITTI Cars Hard
3D Object Proposals for Accurate Object Class Detection
The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving.
Ranked #10 on
Vehicle Pose Estimation
on KITTI Cars Hard
Improving Object Proposals With Multi-Thresholding Straddling Expansion
Based on the characteristics of superpixel tightness distribution, we propose an effective method, namely multi-thresholding straddling expansion (MTSE) to reduce localization bias via fast diversification.
