NeRF-Det++: Incorporating Semantic Cues and Perspective-aware Depth
  Supervision for Indoor Multi-View 3D Detection
Chenxi Huang Yuenan Hou Weicai Ye Di Huang Xiaoshui Huang Binbin Lin Deng Cai Wanli Ouyang
NeRF-Det has achieved impressive performance in indoor multi-view 3D
detection by innovatively utilizing NeRF to enhance representation learning.
Despite its notable performance, we uncover three decisive shortcomings in its
current design, including semantic ambiguity, inappropriate sampling, and
insufficient utilization of depth supervision. To combat the aforementioned
problems, we present three corresponding solutions: 1) Semantic Enhancement. We
project the freely available 3D segmentation annotations onto the 2D plane and
leverage the corresponding 2D semantic maps as the supervision signal,
significantly enhancing the semantic awareness of multi-view detectors. 2)
Perspective-aware Sampling. Instead of employing the uniform sampling strategy,
we put forward the perspective-aware sampling policy that samples densely near
the camera while sparsely in the distance, more effectively collecting the
valuable geometric clues. 3)Ordinal Residual Depth Supervision. As opposed to
directly regressing the depth values that are difficult to optimize, we divide
the depth range of each scene into a fixed number of ordinal bins and
reformulate the depth prediction as the combination of the classification of
depth bins as well as the regression of the residual depth values, thereby
benefiting the depth learning process. The resulting algorithm, NeRF-Det++, has
exhibited appealing performance in the ScanNetV2 and ARKITScenes datasets.
Notably, in ScanNetV2, NeRF-Det++ outperforms the competitive NeRF-Det by +1.9%
in mAP@0.25 and +3.5% in mAP@0.50$. The code will be publicly at
https://github.com/mrsempress/NeRF-Detplusplus.