3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval.
Ranked #1 on Monocular 3D Object Detection on Google Objectron
Deep implicit functions (DIFs), as a kind of 3D shape representation, are becoming more and more popular in the 3D vision community due to their compactness and strong representation power.
Many prior works have focused on _latent-encoded_ neural implicits, where a latent vector encoding of a specific shape is also fed as input.
In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired.
GSNet utilizes a unique four-way feature extraction and fusion scheme and directly regresses 6DoF poses and shapes in a single forward pass.
Ranked #1 on 3D Car Instance Understanding on ApolloCar3D
3D CAR INSTANCE UNDERSTANDING 3D POSE ESTIMATION 3D RECONSTRUCTION 3D SHAPE MODELING 3D SHAPE RECONSTRUCTION FROM A SINGLE 2D IMAGE 3D SHAPE REPRESENTATION 6D POSE ESTIMATION 6D POSE ESTIMATION USING RGB AUTONOMOUS DRIVING KEYPOINT DETECTION SELF-DRIVING CARS VEHICLE KEY-POINT AND ORIENTATION ESTIMATION VEHICLE POSE ESTIMATION
Generative models have proven effective at modeling 3D shapes and their statistical variations.
In this work we address the challenging problem of multiview 3D surface reconstruction.
The network is trained to reconstruct a shape using a set of convexes obtained from a BSP-tree built on a set of planes.