  In this paper, we propose an advanced methodology for the detection of 3D
objects and precise estimation of their spatial positions from a single image.
Unlike conventional frameworks that rely solely on center-point and dimension
predictions, our research leverages a deep convolutional neural network-based
3D object weighted orientation regression paradigm. These estimates are then
seamlessly integrated with geometric constraints obtained from a 2D bounding
box, resulting in derivation of a comprehensive 3D bounding box. Our novel
network design encompasses two key outputs. The first output involves the
estimation of 3D object orientation through the utilization of a
discrete-continuous loss function. Simultaneously, the second output predicts
objectivity-based confidence scores with minimal variance. Additionally, we
also introduce enhancements to our methodology through the incorporation of
lightweight residual feature extractors. By combining the derived estimates
with the geometric constraints inherent in the 2D bounding box, our approach
significantly improves the accuracy of 3D object pose determination, surpassing
baseline methodologies. Our method is rigorously evaluated on the KITTI 3D
object detection benchmark, demonstrating superior performance.

Computer Vision and Pattern Recognition